added evaluation command

2. ui dump now include gcp results
3. showing default option for more args validation process commands

.gitignore

@@ -1,3 +1,11 @@
+/data/
+/model/
+/train/
+.env*
+*.yaml
+*.yml
+*.json
+
 
 # Created by https://www.gitignore.io/api/python
 # Edit at https://www.gitignore.io/?templates=python
@@ -108,3 +116,36 @@ dmypy.json
 .pyre/
 
 # End of https://www.gitignore.io/api/python
+
+# Created by https://www.gitignore.io/api/macos
+# Edit at https://www.gitignore.io/?templates=macos
+
+### macOS ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+# End of https://www.gitignore.io/api/macos

Notes.md

@@ -0,0 +1,5 @@
+
+> Diff after splitting based on type
+```
+diff <(cat data/asr_data/call_upwork_test_cnd_*/manifest.json |sort) <(cat data/asr_data/call_upwork_test_cnd/manifest.json |sort)
+```

jasper/asr.py

@@ -62,7 +62,7 @@ class JasperASR(object):
         wf = wave.open(audio_file_path, "w")
         wf.setnchannels(1)
         wf.setsampwidth(2)
-        wf.setframerate(16000)
+        wf.setframerate(24000)
         wf.writeframesraw(audio_data)
         wf.close()
         manifest = {"audio_filepath": audio_file_path, "duration": 60, "text": "todo"}
@@ -108,6 +108,8 @@ class JasperASR(object):
         tensors = self.neural_factory.infer(tensors=eval_tensors)
         prediction = post_process_predictions(tensors[0], self.labels)
         prediction_text = ". ".join(prediction)
+        os.unlink(manifest_file.name)
+        os.unlink(audio_file.name)
         return prediction_text
 
     def transcribe_file(self, audio_file, *args, **kwargs):

jasper/client.py

@@ -0,0 +1,21 @@
+import os
+import logging
+import rpyc
+from functools import lru_cache
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+ASR_HOST = os.environ.get("JASPER_ASR_RPYC_HOST", "localhost")
+ASR_PORT = int(os.environ.get("JASPER_ASR_RPYC_PORT", "8045"))
+
+
+@lru_cache()
+def transcribe_gen(asr_host=ASR_HOST, asr_port=ASR_PORT):
+    logger.info(f"connecting to asr server at {asr_host}:{asr_port}")
+    asr = rpyc.connect(asr_host, asr_port).root
+    logger.info(f"connected to asr server successfully")
+    return asr.transcribe

jasper/data/__init__.py

@@ -0,0 +1 @@
+

jasper/data/asr_recycler.py

@@ -0,0 +1,104 @@
+import typer
+from itertools import chain
+from io import BytesIO
+from pathlib import Path
+
+app = typer.Typer()
+
+
+@app.command()
+def extract_data(
+    call_audio_dir: Path = Path("/dataset/png_prod/call_audio"),
+    call_meta_dir: Path = Path("/dataset/png_prod/call_metadata"),
+    output_dir: Path = Path("./data"),
+    dataset_name: str = "png_gcp_2jan",
+    verbose: bool = False,
+):
+    from pydub import AudioSegment
+    from .utils import ExtendedPath, asr_data_writer, strip_silence
+    from lenses import lens
+
+    call_asr_data: Path = output_dir / Path("asr_data")
+    call_asr_data.mkdir(exist_ok=True, parents=True)
+
+    def wav_event_generator(call_audio_dir):
+        for wav_path in call_audio_dir.glob("**/*.wav"):
+            if verbose:
+                typer.echo(f"loading events for file {wav_path}")
+            call_wav = AudioSegment.from_file_using_temporary_files(wav_path)
+            rel_meta_path = wav_path.with_suffix(".json").relative_to(call_audio_dir)
+            meta_path = call_meta_dir / rel_meta_path
+            events = ExtendedPath(meta_path).read_json()
+            yield call_wav, wav_path, events
+
+    def contains_asr(x):
+        return "AsrResult" in x
+
+    def channel(n):
+        def filter_func(ev):
+            return (
+                ev["AsrResult"]["Channel"] == n
+                if "Channel" in ev["AsrResult"]
+                else n == 0
+            )
+
+        return filter_func
+
+    def compute_endtime(call_wav, state):
+        for (i, st) in enumerate(state):
+            start_time = st["AsrResult"]["Alternatives"][0].get("StartTime", 0)
+            transcript = st["AsrResult"]["Alternatives"][0]["Transcript"]
+            if i + 1 < len(state):
+                end_time = state[i + 1]["AsrResult"]["Alternatives"][0]["StartTime"]
+            else:
+                end_time = call_wav.duration_seconds
+            full_code_seg = call_wav[start_time * 1000 : end_time * 1000]
+            code_seg = strip_silence(full_code_seg)
+            code_fb = BytesIO()
+            code_seg.export(code_fb, format="wav")
+            code_wav = code_fb.getvalue()
+            # only starting 1 min audio has reliable alignment ignore rest
+            if start_time > 60:
+                if verbose:
+                    print(f'start time over 60 seconds of audio skipping.')
+                break
+            # only if some reasonable audio data is present yield it
+            if code_seg.duration_seconds < 0.5:
+                if verbose:
+                    print(f'transcript chunk "{transcript}" contains no audio skipping.')
+                continue
+            yield transcript, code_seg.duration_seconds, code_wav
+
+    def asr_data_generator(call_wav, call_wav_fname, events):
+        call_wav_0, call_wav_1 = call_wav.split_to_mono()
+        asr_events = lens["Events"].Each()["Event"].Filter(contains_asr)
+        call_evs_0 = asr_events.Filter(channel(0)).collect()(events)
+        # Ignoring agent channel events
+        # call_evs_1 = asr_events.Filter(channel(1)).collect()(events)
+        if verbose:
+            typer.echo(f"processing data points on {call_wav_fname}")
+        call_data_0 = compute_endtime(call_wav_0, call_evs_0)
+        # Ignoring agent channel
+        # call_data_1 = compute_endtime(call_wav_1, call_evs_1)
+        return call_data_0  # chain(call_data_0, call_data_1)
+
+    def generate_call_asr_data():
+        full_asr_data = []
+        total_duration = 0
+        for wav, wav_path, ev in wav_event_generator(call_audio_dir):
+            asr_data = asr_data_generator(wav, wav_path, ev)
+            total_duration += wav.duration_seconds
+            full_asr_data.append(asr_data)
+        typer.echo(f"loaded {len(full_asr_data)} calls of duration {total_duration}s")
+        n_dps = asr_data_writer(call_asr_data, dataset_name, chain(*full_asr_data))
+        typer.echo(f"written {n_dps} data points")
+
+    generate_call_asr_data()
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()

jasper/data/call_recycler.py

@@ -0,0 +1,509 @@
+import typer
+from pathlib import Path
+from enum import Enum
+
+
+app = typer.Typer()
+
+
+@app.command()
+def export_all_logs(
+    call_logs_file: Path = typer.Option(Path("./call_logs.yaml"), show_default=True),
+    domain: str = typer.Option("sia-data.agaralabs.com", show_default=True),
+):
+    from .utils import get_mongo_conn
+    from collections import defaultdict
+    from ruamel.yaml import YAML
+
+    yaml = YAML()
+    mongo_coll = get_mongo_conn()
+    caller_calls = defaultdict(lambda: [])
+    for call in mongo_coll.find():
+        sysid = call["SystemID"]
+        call_uri = f"http://{domain}/calls/{sysid}"
+        caller = call["Caller"]
+        caller_calls[caller].append(call_uri)
+    caller_list = []
+    for caller in caller_calls:
+        caller_list.append({"name": caller, "calls": caller_calls[caller]})
+    output_yaml = {"users": caller_list}
+    typer.echo(f"exporting call logs to yaml file at {call_logs_file}")
+    with call_logs_file.open("w") as yf:
+        yaml.dump(output_yaml, yf)
+
+
+@app.command()
+def export_calls_between(
+    start_cid: str,
+    end_cid: str,
+    call_logs_file: Path = typer.Option(Path("./call_logs.yaml"), show_default=True),
+    domain: str = typer.Option("sia-data.agaralabs.com", show_default=True),
+    mongo_port: int = 27017,
+):
+    from collections import defaultdict
+    from ruamel.yaml import YAML
+    from .utils import get_mongo_conn
+
+    yaml = YAML()
+    mongo_coll = get_mongo_conn(port=mongo_port)
+    start_meta = mongo_coll.find_one({"SystemID": start_cid})
+    end_meta = mongo_coll.find_one({"SystemID": end_cid})
+
+    caller_calls = defaultdict(lambda: [])
+    call_query = mongo_coll.find(
+        {
+            "StartTS": {"$gte": start_meta["StartTS"]},
+            "EndTS": {"$lte": end_meta["EndTS"]},
+        }
+    )
+    for call in call_query:
+        sysid = call["SystemID"]
+        call_uri = f"http://{domain}/calls/{sysid}"
+        caller = call["Caller"]
+        caller_calls[caller].append(call_uri)
+    caller_list = []
+    for caller in caller_calls:
+        caller_list.append({"name": caller, "calls": caller_calls[caller]})
+    output_yaml = {"users": caller_list}
+    typer.echo(f"exporting call logs to yaml file at {call_logs_file}")
+    with call_logs_file.open("w") as yf:
+        yaml.dump(output_yaml, yf)
+
+
+@app.command()
+def copy_metas(
+    call_logs_file: Path = typer.Option(Path("./call_logs.yaml"), show_default=True),
+    output_dir: Path = Path("./data"),
+    meta_dir: Path = Path("/tmp/call_metas"),
+):
+    from lenses import lens
+    from ruamel.yaml import YAML
+    from urllib.parse import urlsplit
+    from shutil import copy2
+
+    yaml = YAML()
+    call_logs = yaml.load(call_logs_file.read_text())
+
+    call_meta_dir: Path = output_dir / Path("call_metas")
+    call_meta_dir.mkdir(exist_ok=True, parents=True)
+    meta_dir.mkdir(exist_ok=True, parents=True)
+
+    def get_cid(uri):
+        return Path(urlsplit(uri).path).stem
+
+    def copy_meta(uri):
+        cid = get_cid(uri)
+        saved_meta_path = call_meta_dir / Path(f"{cid}.json")
+        dest_meta_path = meta_dir / Path(f"{cid}.json")
+        if not saved_meta_path.exists():
+            print(f"{saved_meta_path} not found")
+        copy2(saved_meta_path, dest_meta_path)
+
+    def download_meta_audio():
+        call_lens = lens["users"].Each()["calls"].Each()
+        call_lens.modify(copy_meta)(call_logs)
+
+    download_meta_audio()
+
+
+class ExtractionType(str, Enum):
+    flow = "flow"
+    data = "data"
+
+
+@app.command()
+def analyze(
+    leaderboard: bool = False,
+    plot_calls: bool = False,
+    extract_data: bool = False,
+    extraction_type: ExtractionType = typer.Option(
+        ExtractionType.data, show_default=True
+    ),
+    start_delay: float = 1.5,
+    download_only: bool = False,
+    strip_silent_chunks: bool = True,
+    call_logs_file: Path = typer.Option(Path("./call_logs.yaml"), show_default=True),
+    output_dir: Path = Path("./data"),
+    data_name: str = None,
+    mongo_uri: str = typer.Option(
+        "mongodb://localhost:27017/test.calls", show_default=True
+    ),
+):
+
+    from urllib.parse import urlsplit
+    from functools import reduce
+    import boto3
+    from io import BytesIO
+    import json
+    from ruamel.yaml import YAML
+    import re
+    from google.protobuf.timestamp_pb2 import Timestamp
+    from datetime import timedelta
+    import librosa
+    import librosa.display
+    from lenses import lens
+    from pprint import pprint
+    import pandas as pd
+    import matplotlib.pyplot as plt
+    import matplotlib
+    from tqdm import tqdm
+    from .utils import ui_dump_manifest_writer, strip_silence, get_mongo_coll, get_call_logs
+    from pydub import AudioSegment
+    from natural.date import compress
+
+    matplotlib.rcParams["agg.path.chunksize"] = 10000
+
+    matplotlib.use("agg")
+
+    yaml = YAML()
+    s3 = boto3.client("s3")
+    mongo_collection = get_mongo_coll(mongo_uri)
+    call_media_dir: Path = output_dir / Path("call_wavs")
+    call_media_dir.mkdir(exist_ok=True, parents=True)
+    call_meta_dir: Path = output_dir / Path("call_metas")
+    call_meta_dir.mkdir(exist_ok=True, parents=True)
+    call_plot_dir: Path = output_dir / Path("plots")
+    call_plot_dir.mkdir(exist_ok=True, parents=True)
+    call_asr_data: Path = output_dir / Path("asr_data")
+    call_asr_data.mkdir(exist_ok=True, parents=True)
+    dataset_name = call_logs_file.stem if not data_name else data_name
+
+    call_logs = yaml.load(call_logs_file.read_text())
+
+    def gen_ev_fev_timedelta(fev):
+        fev_p = Timestamp()
+        fev_p.FromJsonString(fev["CreatedTS"])
+        fev_dt = fev_p.ToDatetime()
+        td_0 = timedelta()
+
+        def get_timedelta(ev):
+            ev_p = Timestamp()
+            ev_p.FromJsonString(value=ev["CreatedTS"])
+            ev_dt = ev_p.ToDatetime()
+            delta = ev_dt - fev_dt
+            return delta if delta > td_0 else td_0
+
+        return get_timedelta
+
+    def chunk_n(evs, n):
+        return [evs[i * n : (i + 1) * n] for i in range((len(evs) + n - 1) // n)]
+
+    if extraction_type == ExtractionType.data:
+
+        def is_utter_event(ev):
+            return (
+                (ev["Author"] == "CONV" or ev["Author"] == "ASR")
+                and (ev["Type"] != "DEBUG")
+                and ev["Type"] != "ASR_RESULT"
+            )
+
+        def get_data_points(utter_events, td_fn):
+            data_points = []
+            for evs in chunk_n(utter_events, 3):
+                try:
+                    assert evs[0]["Type"] == "CONV_RESULT"
+                    assert evs[1]["Type"] == "STARTED_SPEAKING"
+                    assert evs[2]["Type"] == "STOPPED_SPEAKING"
+                    start_time = td_fn(evs[1]).total_seconds() - start_delay
+                    end_time = td_fn(evs[2]).total_seconds()
+                    spoken = evs[0]["Msg"]
+                    data_points.append(
+                        {"start_time": start_time, "end_time": end_time, "code": spoken}
+                    )
+                except AssertionError:
+                    # skipping invalid data_points
+                    pass
+            return data_points
+
+        def text_extractor(spoken):
+            return (
+                re.search(r"'(.*)'", spoken).groups(0)[0]
+                if len(spoken) > 6 and re.search(r"'(.*)'", spoken)
+                else spoken
+            )
+
+    elif extraction_type == ExtractionType.flow:
+
+        def is_final_asr_event_or_spoken(ev):
+            pld = json.loads(ev["Payload"])
+            return (
+                pld["AsrResult"]["Results"][0]["IsFinal"]
+                if ev["Type"] == "ASR_RESULT"
+                else True
+            )
+
+        def is_utter_event(ev):
+            return (
+                ev["Author"] == "CONV"
+                or (ev["Author"] == "ASR" and is_final_asr_event_or_spoken(ev))
+            ) and (ev["Type"] != "DEBUG")
+
+        def get_data_points(utter_events, td_fn):
+            data_points = []
+            for evs in chunk_n(utter_events, 4):
+                try:
+                    assert len(evs) == 4
+                    assert evs[0]["Type"] == "CONV_RESULT"
+                    assert evs[1]["Type"] == "STARTED_SPEAKING"
+                    assert evs[2]["Type"] == "ASR_RESULT"
+                    assert evs[3]["Type"] == "STOPPED_SPEAKING"
+                    start_time = td_fn(evs[1]).total_seconds() - start_delay
+                    end_time = td_fn(evs[2]).total_seconds()
+                    conv_msg = evs[0]["Msg"]
+                    if "full name" in conv_msg.lower():
+                        pld = json.loads(evs[2]["Payload"])
+                        spoken = pld["AsrResult"]["Results"][0]["Alternatives"][0][
+                            "Transcript"
+                        ]
+                        data_points.append(
+                            {
+                                "start_time": start_time,
+                                "end_time": end_time,
+                                "code": spoken,
+                            }
+                        )
+                except AssertionError:
+                    # skipping invalid data_points
+                    pass
+            return data_points
+
+        def text_extractor(spoken):
+            return spoken
+
+    def process_call(call_obj):
+        call_meta = get_call_logs(call_obj, s3, call_meta_dir)
+        call_events = call_meta["Events"]
+
+        def is_writer_uri_event(ev):
+            return ev["Author"] == "AUDIO_WRITER" and "s3://" in ev["Msg"]
+
+        writer_events = list(filter(is_writer_uri_event, call_events))
+        s3_wav_url = re.search(r"(s3://.*)", writer_events[0]["Msg"]).groups(0)[0]
+        s3_wav_url_p = urlsplit(s3_wav_url)
+
+        def is_first_audio_ev(state, ev):
+            if state[0]:
+                return state
+            else:
+                return (ev["Author"] == "GATEWAY" and ev["Type"] == "AUDIO", ev)
+
+        (_, first_audio_ev) = reduce(is_first_audio_ev, call_events, (False, {}))
+
+        get_ev_fev_timedelta = gen_ev_fev_timedelta(first_audio_ev)
+
+        uevs = list(filter(is_utter_event, call_events))
+        ev_count = len(uevs)
+        utter_events = uevs[: ev_count - ev_count % 3]
+        saved_wav_path = call_media_dir / Path(Path(s3_wav_url_p.path).name)
+        if not saved_wav_path.exists():
+            print(f"downloading : {saved_wav_path} from {s3_wav_url}")
+            s3.download_file(
+                s3_wav_url_p.netloc, s3_wav_url_p.path[1:], str(saved_wav_path)
+            )
+
+        return {
+            "wav_path": saved_wav_path,
+            "num_samples": len(utter_events) // 3,
+            "meta": call_obj,
+            "first_event_fn": get_ev_fev_timedelta,
+            "utter_events": utter_events,
+        }
+
+    def get_cid(uri):
+        return Path(urlsplit(uri).path).stem
+
+    def ensure_call(uri):
+        cid = get_cid(uri)
+        meta = mongo_collection.find_one({"SystemID": cid})
+        process_meta = process_call(meta)
+        return process_meta
+
+    def retrieve_processed_callmeta(uri):
+        cid = get_cid(uri)
+        meta = mongo_collection.find_one({"SystemID": cid})
+        duration = meta["EndTS"] - meta["StartTS"]
+        process_meta = process_call(meta)
+        data_points = get_data_points(
+            process_meta["utter_events"], process_meta["first_event_fn"]
+        )
+        process_meta["data_points"] = data_points
+        return {"url": uri, "meta": meta, "duration": duration, "process": process_meta}
+
+    def retrieve_callmeta(call_uri):
+        uri = call_uri["call_uri"]
+        name = call_uri["name"]
+        cid = get_cid(uri)
+        meta = mongo_collection.find_one({"SystemID": cid})
+        duration = meta["EndTS"] - meta["StartTS"]
+        process_meta = process_call(meta)
+        data_points = get_data_points(
+            process_meta["utter_events"], process_meta["first_event_fn"]
+        )
+        process_meta["data_points"] = data_points
+        return {
+            "url": uri,
+            "name": name,
+            "meta": meta,
+            "duration": duration,
+            "process": process_meta,
+        }
+
+    def download_meta_audio():
+        call_lens = lens["users"].Each()["calls"].Each()
+        call_lens.modify(ensure_call)(call_logs)
+
+    def plot_calls_data():
+        def plot_data_points(y, sr, data_points, file_path):
+            plt.figure(figsize=(16, 12))
+            librosa.display.waveplot(y=y, sr=sr)
+            for dp in data_points:
+                start, end, code = dp["start_time"], dp["end_time"], dp["code"]
+                plt.axvspan(start, end, color="green", alpha=0.2)
+                text_pos = (start + end) / 2
+                plt.text(
+                    text_pos,
+                    0.25,
+                    f"{code}",
+                    rotation=90,
+                    horizontalalignment="center",
+                    verticalalignment="center",
+                )
+            plt.title("Datapoints")
+            plt.savefig(file_path, format="png")
+            return file_path
+
+        def plot_call(call_obj):
+            saved_wav_path, data_points, sys_id = (
+                call_obj["process"]["wav_path"],
+                call_obj["process"]["data_points"],
+                call_obj["meta"]["SystemID"],
+            )
+            file_path = call_plot_dir / Path(sys_id).with_suffix(".png")
+            if not file_path.exists():
+                print(f"plotting: {file_path}")
+                (y, sr) = librosa.load(saved_wav_path)
+                plot_data_points(y, sr, data_points, str(file_path))
+            return file_path
+
+        call_lens = lens["users"].Each()["calls"].Each()
+        call_stats = call_lens.modify(retrieve_processed_callmeta)(call_logs)
+        # call_plot_data = call_lens.collect()(call_stats)
+        call_plots = call_lens.modify(plot_call)(call_stats)
+        # with ThreadPoolExecutor(max_workers=20) as exe:
+        #     print('starting all plot tasks')
+        #     responses = [exe.submit(plot_call, w) for w in call_plot_data]
+        #     print('submitted all plot tasks')
+        #     call_plots = [r.result() for r in responses]
+        pprint(call_plots)
+
+    def extract_data_points():
+        if strip_silent_chunks:
+
+            def audio_process(seg):
+                return strip_silence(seg)
+
+        else:
+
+            def audio_process(seg):
+                return seg
+
+        def gen_data_values(saved_wav_path, data_points, caller_name):
+            call_seg = (
+                AudioSegment.from_wav(saved_wav_path)
+                .set_channels(1)
+                .set_sample_width(2)
+                .set_frame_rate(24000)
+            )
+            for dp_id, dp in enumerate(data_points):
+                start, end, spoken = dp["start_time"], dp["end_time"], dp["code"]
+                spoken_seg = audio_process(call_seg[start * 1000 : end * 1000])
+                spoken_fb = BytesIO()
+                spoken_seg.export(spoken_fb, format="wav")
+                spoken_wav = spoken_fb.getvalue()
+                # search for actual pnr code and handle plain codes as well
+                extracted_code = text_extractor(spoken)
+                if strip_silent_chunks and spoken_seg.duration_seconds < 0.5:
+                    print(f'transcript chunk "{spoken}" contains no audio skipping.')
+                    continue
+                yield extracted_code, spoken_seg.duration_seconds, spoken_wav, caller_name, spoken_seg
+
+        call_lens = lens["users"].Each()["calls"].Each()
+
+        def assign_user_call(uc):
+            return (
+                lens["calls"]
+                .Each()
+                .modify(lambda c: {"call_uri": c, "name": uc["name"]})(uc)
+            )
+
+        user_call_logs = lens["users"].Each().modify(assign_user_call)(call_logs)
+        call_stats = call_lens.modify(retrieve_callmeta)(user_call_logs)
+        call_objs = call_lens.collect()(call_stats)
+
+        def data_source():
+            for call_obj in tqdm(call_objs):
+                saved_wav_path, data_points, name = (
+                    call_obj["process"]["wav_path"],
+                    call_obj["process"]["data_points"],
+                    call_obj["name"],
+                )
+                for dp in gen_data_values(saved_wav_path, data_points, name):
+                    yield dp
+
+        ui_dump_manifest_writer(call_asr_data, dataset_name, data_source())
+
+    def show_leaderboard():
+        def compute_user_stats(call_stat):
+            n_samples = (
+                lens["calls"].Each()["process"]["num_samples"].get_monoid()(call_stat)
+            )
+            n_duration = lens["calls"].Each()["duration"].get_monoid()(call_stat)
+            return {
+                "num_samples": n_samples,
+                "duration": n_duration.total_seconds(),
+                "samples_rate": n_samples / n_duration.total_seconds(),
+                "duration_str": compress(n_duration, pad=" "),
+                "name": call_stat["name"],
+            }
+
+        call_lens = lens["users"].Each()["calls"].Each()
+        call_stats = call_lens.modify(retrieve_processed_callmeta)(call_logs)
+        user_stats = lens["users"].Each().modify(compute_user_stats)(call_stats)
+        leader_df = (
+            pd.DataFrame(user_stats["users"])
+            .sort_values(by=["duration"], ascending=False)
+            .reset_index(drop=True)
+        )
+        leader_df["rank"] = leader_df.index + 1
+        leader_board = leader_df.rename(
+            columns={
+                "rank": "Rank",
+                "num_samples": "Count",
+                "name": "Name",
+                "samples_rate": "SpeechRate",
+                "duration_str": "Duration",
+            }
+        )[["Rank", "Name", "Count", "Duration"]]
+        print(
+            """ASR Dataset Leaderboard  :
+---------------------------------"""
+        )
+        print(leader_board.to_string(index=False))
+
+    if download_only:
+        download_meta_audio()
+        return
+    if leaderboard:
+        show_leaderboard()
+    if plot_calls:
+        plot_calls_data()
+    if extract_data:
+        extract_data_points()
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()

jasper/data/conv_generator.py

@@ -0,0 +1,27 @@
+import typer
+from pathlib import Path
+from .utils import generate_dates
+
+app = typer.Typer()
+
+
+@app.command()
+def export_conv_json(
+    conv_src: Path = typer.Option(Path("./conv_data.json"), show_default=True),
+    conv_dest: Path = typer.Option(Path("./data/conv_data.json"), show_default=True),
+):
+    from .utils import ExtendedPath
+
+    conv_data = ExtendedPath(conv_src).read_json()
+
+    conv_data["dates"] = generate_dates()
+
+    ExtendedPath(conv_dest).write_json(conv_data)
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()

jasper/data/nlu_generator.py

@@ -0,0 +1,98 @@
+from pathlib import Path
+
+import typer
+import pandas as pd
+from ruamel.yaml import YAML
+from itertools import product
+from .utils import generate_dates
+
+app = typer.Typer()
+
+
+def unique_entity_list(entity_template_tags, entity_data):
+    unique_entity_set = {
+        t
+        for n in range(1, 5)
+        for t in entity_data[f"Answer.utterance-{n}"].tolist()
+        if any(et in t for et in entity_template_tags)
+    }
+    return list(unique_entity_set)
+
+
+def nlu_entity_reader(nlu_data_file: Path = Path("./nlu_data.yaml")):
+    yaml = YAML()
+    nlu_data = yaml.load(nlu_data_file.read_text())
+    for cf in nlu_data["csv_files"]:
+        data = pd.read_csv(cf["fname"])
+        for et in cf["entities"]:
+            entity_name = et["name"]
+            entity_template_tags = et["tags"]
+            if "filter" in et:
+                entity_data = data[data[cf["filter_key"]] == et["filter"]]
+            else:
+                entity_data = data
+            yield entity_name, entity_template_tags, entity_data
+
+
+def nlu_samples_reader(nlu_data_file: Path = Path("./nlu_data.yaml")):
+    yaml = YAML()
+    nlu_data = yaml.load(nlu_data_file.read_text())
+    sm = {s["name"]: s for s in nlu_data["samples_per_entity"]}
+    return sm
+
+
+@app.command()
+def compute_unique_nlu_stats(
+    nlu_data_file: Path = typer.Option(Path("./nlu_data.yaml"), show_default=True),
+):
+    for entity_name, entity_template_tags, entity_data in nlu_entity_reader(
+        nlu_data_file
+    ):
+        entity_count = len(unique_entity_list(entity_template_tags, entity_data))
+        print(f"{entity_name}\t{entity_count}")
+
+
+def replace_entity(tmpl, value, tags):
+    result = tmpl
+    for t in tags:
+        result = result.replace(t, value)
+    return result
+
+
+@app.command()
+def export_nlu_conv_json(
+    conv_src: Path = typer.Option(Path("./conv_data.json"), show_default=True),
+    conv_dest: Path = typer.Option(Path("./data/conv_data.json"), show_default=True),
+    nlu_data_file: Path = typer.Option(Path("./nlu_data.yaml"), show_default=True),
+):
+    from .utils import ExtendedPath
+    from random import sample
+
+    entity_samples = nlu_samples_reader(nlu_data_file)
+    conv_data = ExtendedPath(conv_src).read_json()
+    conv_data["Dates"] = generate_dates()
+    result_dict = {}
+    data_count = 0
+    for entity_name, entity_template_tags, entity_data in nlu_entity_reader(
+        nlu_data_file
+    ):
+        entity_variants = sample(conv_data[entity_name], entity_samples[entity_name]["test_size"])
+        unique_entites = unique_entity_list(entity_template_tags, entity_data)
+        # sample_entites = sample(unique_entites, entity_samples[entity_name]["samples"])
+        result_dict[entity_name] = []
+        for val in entity_variants:
+            sample_entites = sample(unique_entites, entity_samples[entity_name]["samples"])
+            for tmpl in sample_entites:
+                result = replace_entity(tmpl, val, entity_template_tags)
+                result_dict[entity_name].append(result)
+                data_count += 1
+    print(f"Total of {data_count} variants generated")
+    ExtendedPath(conv_dest).write_json(result_dict)
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()

jasper/data/process.py

@@ -0,0 +1,77 @@
+import json
+from pathlib import Path
+from sklearn.model_selection import train_test_split
+from .utils import asr_manifest_reader, asr_manifest_writer
+from typing import List
+from itertools import chain
+import typer
+
+app = typer.Typer()
+
+
+@app.command()
+def fixate_data(dataset_path: Path):
+    manifest_path = dataset_path / Path("manifest.json")
+    real_manifest_path = dataset_path / Path("abs_manifest.json")
+
+    def fix_path():
+        for i in asr_manifest_reader(manifest_path):
+            i["audio_filepath"] = str(dataset_path / Path(i["audio_filepath"]))
+            yield i
+
+    asr_manifest_writer(real_manifest_path, fix_path())
+
+
+@app.command()
+def augment_data(src_dataset_paths: List[Path], dest_dataset_path: Path):
+    reader_list = []
+    abs_manifest_path = Path("abs_manifest.json")
+    for dataset_path in src_dataset_paths:
+        manifest_path = dataset_path / abs_manifest_path
+        reader_list.append(asr_manifest_reader(manifest_path))
+    dest_dataset_path.mkdir(parents=True, exist_ok=True)
+    dest_manifest_path = dest_dataset_path / abs_manifest_path
+    asr_manifest_writer(dest_manifest_path, chain(*reader_list))
+
+
+@app.command()
+def split_data(dataset_path: Path, test_size: float = 0.1):
+    manifest_path = dataset_path / Path("abs_manifest.json")
+    asr_data = list(asr_manifest_reader(manifest_path))
+    train_pnr, test_pnr = train_test_split(asr_data, test_size=test_size)
+    asr_manifest_writer(manifest_path.with_name("train_manifest.json"), train_pnr)
+    asr_manifest_writer(manifest_path.with_name("test_manifest.json"), test_pnr)
+
+
+@app.command()
+def validate_data(dataset_path: Path):
+    from natural.date import compress
+    from datetime import timedelta
+
+    for mf_type in ["train_manifest.json", "test_manifest.json"]:
+        data_file = dataset_path / Path(mf_type)
+        print(f"validating {data_file}.")
+        with Path(data_file).open("r") as pf:
+            pnr_jsonl = pf.readlines()
+        duration = 0
+        for (i, s) in enumerate(pnr_jsonl):
+            try:
+                d = json.loads(s)
+                duration += d["duration"]
+                audio_file = data_file.parent / Path(d["audio_filepath"])
+                if not audio_file.exists():
+                    raise OSError(f"File {audio_file} not found")
+            except BaseException as e:
+                print(f'failed on {i} with "{e}"')
+        duration_str = compress(timedelta(seconds=duration), pad=" ")
+        print(
+            f"no errors found. seems like a valid {mf_type}. contains {duration_str}sec of audio"
+        )
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()

jasper/data/rev_recycler.py

@@ -0,0 +1,175 @@
+import typer
+from itertools import chain
+from io import BytesIO
+from pathlib import Path
+import re
+
+app = typer.Typer()
+
+
+@app.command()
+def extract_data(
+    call_audio_dir: Path = typer.Option(Path("/dataset/rev/wavs"), show_default=True),
+    call_meta_dir: Path = typer.Option(Path("/dataset/rev/jsons"), show_default=True),
+    output_dir: Path = typer.Option(Path("./data"), show_default=True),
+    dataset_name: str = typer.Option("rev_transribed", show_default=True),
+    verbose: bool = False,
+):
+    from pydub import AudioSegment
+    from .utils import ExtendedPath, asr_data_writer, strip_silence
+    from lenses import lens
+    import datetime
+
+    call_asr_data: Path = output_dir / Path("asr_data")
+    call_asr_data.mkdir(exist_ok=True, parents=True)
+
+    def wav_event_generator(call_audio_dir):
+        for wav_path in call_audio_dir.glob("**/*.wav"):
+            if verbose:
+                typer.echo(f"loading events for file {wav_path}")
+            call_wav = AudioSegment.from_file_using_temporary_files(wav_path)
+            rel_meta_path = wav_path.with_suffix(".json").relative_to(call_audio_dir)
+            meta_path = call_meta_dir / rel_meta_path
+            if meta_path.exists():
+                events = ExtendedPath(meta_path).read_json()
+                yield call_wav, wav_path, events
+            else:
+                if verbose:
+                    typer.echo(f"missing json corresponding to {wav_path}")
+
+    def contains_asr(x):
+        return "AsrResult" in x
+
+    def channel(n):
+        def filter_func(ev):
+            return (
+                ev["AsrResult"]["Channel"] == n
+                if "Channel" in ev["AsrResult"]
+                else n == 0
+            )
+
+        return filter_func
+
+    def time_to_msecs(time_str):
+        return (
+            datetime.datetime.strptime(time_str, "%H:%M:%S,%f")
+            - datetime.datetime(1900, 1, 1)
+        ).total_seconds() * 1000
+
+    def process_utterance_chunk(wav_seg, start_time, end_time, monologue):
+        # offset by 1sec left side to include vad? discarded audio
+        full_tscript_wav_seg = wav_seg[
+            time_to_msecs(start_time) - 1000 : time_to_msecs(end_time)  # + 1000
+        ]
+        tscript_wav_seg = strip_silence(full_tscript_wav_seg)
+        tscript_wav_fb = BytesIO()
+        tscript_wav_seg.export(tscript_wav_fb, format="wav")
+        tscript_wav = tscript_wav_fb.getvalue()
+        text = "".join(lens["elements"].Each()["value"].collect()(monologue))
+        text_clean = re.sub(r"\[.*\]", "", text)
+        return tscript_wav, tscript_wav_seg.duration_seconds, text_clean
+
+    def dual_asr_data_generator(wav_seg, wav_path, meta):
+        left_audio, right_audio = wav_seg.split_to_mono()
+        channel_map = {"Agent": right_audio, "Client": left_audio}
+        monologues = lens["monologues"].Each().collect()(meta)
+        for monologue in monologues:
+            # print(monologue["speaker_name"])
+            speaker_channel = channel_map.get(monologue["speaker_name"])
+            if not speaker_channel:
+                if verbose:
+                    print(
+                        f'unknown speaker tag {monologue["speaker_name"]} in wav:{wav_path} skipping.'
+                    )
+                continue
+            try:
+                start_time = (
+                    lens["elements"]
+                    .Each()
+                    .Filter(lambda x: "timestamp" in x)["timestamp"]
+                    .collect()(monologue)[0]
+                )
+                end_time = (
+                    lens["elements"]
+                    .Each()
+                    .Filter(lambda x: "end_timestamp" in x)["end_timestamp"]
+                    .collect()(monologue)[-1]
+                )
+            except IndexError:
+                if verbose:
+                    print(
+                        f"error when loading timestamp events in wav:{wav_path} skipping."
+                    )
+                continue
+            tscript_wav, seg_dur, text_clean = process_utterance_chunk(
+                speaker_channel, start_time, end_time, monologue
+            )
+            if seg_dur < 0.5:
+                if verbose:
+                    print(
+                        f'transcript chunk "{text_clean}" contains no audio in {wav_path} skipping.'
+                    )
+                continue
+            yield text_clean, seg_dur, tscript_wav
+
+    def mono_asr_data_generator(wav_seg, wav_path, meta):
+        monologues = lens["monologues"].Each().collect()(meta)
+        for monologue in monologues:
+            try:
+                start_time = (
+                    lens["elements"]
+                    .Each()
+                    .Filter(lambda x: "timestamp" in x)["timestamp"]
+                    .collect()(monologue)[0]
+                )
+                end_time = (
+                    lens["elements"]
+                    .Each()
+                    .Filter(lambda x: "end_timestamp" in x)["end_timestamp"]
+                    .collect()(monologue)[-1]
+                )
+            except IndexError:
+                if verbose:
+                    print(
+                        f"error when loading timestamp events in wav:{wav_path} skipping."
+                    )
+                continue
+
+            tscript_wav, seg_dur, text_clean = process_utterance_chunk(
+                wav_seg, start_time, end_time, monologue
+            )
+            if seg_dur < 0.5:
+                if verbose:
+                    print(
+                        f'transcript chunk "{text_clean}" contains no audio in {wav_path} skipping.'
+                    )
+                continue
+            yield text_clean, seg_dur, tscript_wav
+
+    def generate_rev_asr_data():
+        full_asr_data = []
+        total_duration = 0
+        for wav, wav_path, ev in wav_event_generator(call_audio_dir):
+            if wav.channels > 2:
+                print(f"skipping many channel audio {wav_path}")
+            asr_data_generator = (
+                mono_asr_data_generator
+                if wav.channels == 1
+                else dual_asr_data_generator
+            )
+            asr_data = asr_data_generator(wav, wav_path, ev)
+            total_duration += wav.duration_seconds
+            full_asr_data.append(asr_data)
+        typer.echo(f"loaded {len(full_asr_data)} calls of duration {total_duration}s")
+        n_dps = asr_data_writer(call_asr_data, dataset_name, chain(*full_asr_data))
+        typer.echo(f"written {n_dps} data points")
+
+    generate_rev_asr_data()
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()

jasper/data/server.py

@@ -0,0 +1,57 @@
+import os
+from pathlib import Path
+
+import typer
+import rpyc
+from rpyc.utils.server import ThreadedServer
+import nemo
+import pickle
+
+# import nemo.collections.asr as nemo_asr
+from nemo.collections.asr.parts.segment import AudioSegment
+
+app = typer.Typer()
+
+nemo.core.NeuralModuleFactory(
+    backend=nemo.core.Backend.PyTorch, placement=nemo.core.DeviceType.CPU
+)
+
+
+class ASRDataService(rpyc.Service):
+    def exposed_get_path_samples(
+        self, file_path, target_sr, int_values, offset, duration, trim
+    ):
+        print(f"loading.. {file_path}")
+        audio = AudioSegment.from_file(
+            file_path,
+            target_sr=target_sr,
+            int_values=int_values,
+            offset=offset,
+            duration=duration,
+            trim=trim,
+        )
+        # print(f"returning.. {len(audio.samples)} items of type{type(audio.samples)}")
+        return pickle.dumps(audio.samples)
+
+    def exposed_read_path(self, file_path):
+        # print(f"reading path.. {file_path}")
+        return Path(file_path).read_bytes()
+
+
+@app.command()
+def run_server(port: int = 0):
+    listen_port = port if port else int(os.environ.get("ASR_DARA_RPYC_PORT", "8064"))
+    service = ASRDataService()
+    t = ThreadedServer(
+        service, port=listen_port, protocol_config={"allow_all_attrs": True}
+    )
+    typer.echo(f"starting asr server on {listen_port}...")
+    t.start()
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()

jasper/data/slu_evaluator.py

@@ -0,0 +1,180 @@
+import typer
+from pathlib import Path
+import json
+
+# from .utils import generate_dates, asr_test_writer
+
+app = typer.Typer()
+
+
+def run_test(reg_path, coll, s3, call_meta_dir, city_code, test_path):
+    from time import sleep
+    import subprocess
+    from .utils import ExtendedPath, get_call_logs
+
+    coll.delete_many({"CallID": test_path.name})
+    # test_path = dump_dir / data_name / test_file
+    # "../saas_reg/regression/run.sh  -f data/asr_data/call_upwork_test_cnd_cities/asr_test.reg"
+    test_output = subprocess.run(
+        ["/bin/bash", "-c", f"{str(reg_path)} --addr [::]:15400 -f {str(test_path)}"]
+    )
+    if test_output.returncode != 0:
+        print("Error running test {test_file}")
+        return
+
+    def get_meta():
+        call_meta = coll.find_one({"CallID": test_path.name})
+        if call_meta:
+            return call_meta
+        else:
+            sleep(2)
+            return get_meta()
+
+    call_meta = get_meta()
+    call_logs = get_call_logs(call_meta, s3, call_meta_dir)
+    call_events = call_logs["Events"]
+
+    test_data_path = test_path.with_suffix(".result.json")
+    test_data = ExtendedPath(test_data_path).read_json()
+
+    def is_final_asr_event_or_spoken(ev):
+        pld = json.loads(ev["Payload"])
+        return (
+            pld["AsrResult"]["Results"][0]["IsFinal"]
+            if ev["Type"] == "ASR_RESULT"
+            else False
+        )
+
+    def is_test_event(ev):
+        return (
+            ev["Author"] == "NLU"
+            or (ev["Author"] == "ASR" and is_final_asr_event_or_spoken(ev))
+        ) and (ev["Type"] != "DEBUG")
+
+    test_evs = list(filter(is_test_event, call_events))
+    if len(test_evs) == 2:
+        try:
+            asr_payload = test_evs[0]["Payload"]
+            asr_result = json.loads(asr_payload)["AsrResult"]["Results"][0]
+            alt_tscripts = [alt["Transcript"] for alt in asr_result["Alternatives"]]
+            gcp_result = "|".join(alt_tscripts)
+            entity_asr = asr_result["AsrDynamicResults"][0]["Candidate"]["Transcript"]
+            nlu_payload = test_evs[1]["Payload"]
+            nlu_result_payload = json.loads(nlu_payload)["NluResults"]
+            entity = test_data[0]["entity"]
+            text = test_data[0]["text"]
+            audio_filepath = test_data[0]["audio_filepath"]
+            pretrained_asr = test_data[0]["pretrained_asr"]
+            nlu_entity = list(json.loads(nlu_result_payload)["Entities"].values())[0]
+            asr_entity = city_code[entity] if entity in city_code else "UNKNOWN"
+            entities_match = asr_entity == nlu_entity
+            result = "Success" if entities_match else "Fail"
+            return {
+                "expected_entity": entity,
+                "text": text,
+                "audio_filepath": audio_filepath,
+                "pretrained_asr": pretrained_asr,
+                "entity_asr": entity_asr,
+                "google_asr": gcp_result,
+                "nlu_result": nlu_result_payload,
+                "asr_entity": asr_entity,
+                "nlu_entity": nlu_entity,
+                "result": result,
+            }
+        except Exception:
+            return {
+                "expected_entity": test_data[0]["entity"],
+                "text": test_data[0]["text"],
+                "audio_filepath": test_data[0]["audio_filepath"],
+                "pretrained_asr": test_data[0]["pretrained_asr"],
+                "entity_asr": "",
+                "google_asr": "",
+                "nlu_result": "",
+                "asr_entity": "",
+                "nlu_entity": "",
+                "result": "Error",
+            }
+    else:
+        return {
+            "expected_entity": test_data[0]["entity"],
+            "text": test_data[0]["text"],
+            "audio_filepath": test_data[0]["audio_filepath"],
+            "pretrained_asr": test_data[0]["pretrained_asr"],
+            "entity_asr": "",
+            "google_asr": "",
+            "nlu_result": "",
+            "asr_entity": "",
+            "nlu_entity": "",
+            "result": "Empty",
+        }
+
+
+@app.command()
+def evaluate_slu(
+    # conv_src: Path = typer.Option(Path("./conv_data.json"), show_default=True),
+    data_name: str = typer.Option("call_upwork_test_cnd_cities", show_default=True),
+    # extraction_key: str = "Cities",
+    dump_dir: Path = Path("./data/asr_data"),
+    call_meta_dir: Path = Path("./data/call_metas"),
+    test_file_pref: str = "asr_test",
+    mongo_uri: str = typer.Option(
+        "mongodb://localhost:27017/test.calls", show_default=True
+    ),
+    test_results: Path = Path("./data/results.csv"),
+    airport_codes: Path = Path("./airports_code.csv"),
+    reg_path: Path = Path("../saas_reg/regression/run.sh"),
+    test_id: str = "5ef481f27031edf6910e94e0",
+):
+    # import json
+    from .utils import get_mongo_coll
+    import pandas as pd
+    import boto3
+    from concurrent.futures import ThreadPoolExecutor
+    from functools import partial
+
+    # import subprocess
+    # from time import sleep
+    import csv
+    from tqdm import tqdm
+
+    s3 = boto3.client("s3")
+    df = pd.read_csv(airport_codes)[["iata", "city"]]
+    city_code = pd.Series(df["iata"].values, index=df["city"]).to_dict()
+
+    test_files = list((dump_dir / data_name).glob(test_file_pref + "*.reg"))
+    coll = get_mongo_coll(mongo_uri)
+    with test_results.open("w") as csvfile:
+        fieldnames = [
+            "expected_entity",
+            "text",
+            "audio_filepath",
+            "pretrained_asr",
+            "entity_asr",
+            "google_asr",
+            "nlu_result",
+            "asr_entity",
+            "nlu_entity",
+            "result",
+        ]
+        writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
+        writer.writeheader()
+        with ThreadPoolExecutor(max_workers=8) as exe:
+            print("starting all loading tasks")
+            for test_result in tqdm(
+                exe.map(
+                    partial(run_test, reg_path, coll, s3, call_meta_dir, city_code),
+                    test_files,
+                ),
+                position=0,
+                leave=True,
+                total=len(test_files),
+            ):
+                writer.writerow(test_result)
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()

jasper/data/test_generator.py

@@ -0,0 +1,99 @@
+import typer
+from pathlib import Path
+from .utils import generate_dates, asr_test_writer
+
+app = typer.Typer()
+
+
+@app.command()
+def export_test_reg(
+    conv_src: Path = typer.Option(Path("./conv_data.json"), show_default=True),
+    data_name: str = typer.Option("call_upwork_test_cnd_cities", show_default=True),
+    extraction_key: str = "Cities",
+    dump_dir: Path = Path("./data/asr_data"),
+    dump_file: Path = Path("ui_dump.json"),
+    manifest_file: Path = Path("manifest.json"),
+    test_file: Path = Path("asr_test.reg"),
+):
+    from .utils import (
+        ExtendedPath,
+        asr_manifest_reader,
+        gcp_transcribe_gen,
+        parallel_apply,
+    )
+    from ..client import transcribe_gen
+    from pydub import AudioSegment
+    from queue import PriorityQueue
+
+    jasper_map = {
+        "PNRs": 8045,
+        "Cities": 8046,
+        "Names": 8047,
+        "Dates": 8048,
+    }
+    # jasper_map = {"PNRs": 8050, "Cities": 8050, "Names": 8050, "Dates": 8050}
+    transcriber_gcp = gcp_transcribe_gen()
+    transcriber_trained = transcribe_gen(asr_port=jasper_map[extraction_key])
+    transcriber_all_trained = transcribe_gen(asr_port=8050)
+    transcriber_libri_all_trained = transcribe_gen(asr_port=8051)
+
+    def find_ent(dd, conv_data):
+        ents = PriorityQueue()
+        for ent in conv_data:
+            if ent in dd["text"]:
+                ents.put((-len(ent), ent))
+        return ents.get_nowait()[1]
+
+    def process_data(d):
+        orig_seg = AudioSegment.from_wav(d["audio_path"])
+        jas_seg = orig_seg.set_channels(1).set_sample_width(2).set_frame_rate(24000)
+        gcp_seg = orig_seg.set_channels(1).set_sample_width(2).set_frame_rate(16000)
+        deepgram_file = Path("/home/shubham/voice_auto/pnrs/wav/") / Path(
+            d["audio_path"].stem + ".txt"
+        )
+        if deepgram_file.exists():
+            d["deepgram"] = "".join(
+                [s.replace("CHANNEL 0:", "") for s in deepgram_file.read_text().split("\n")]
+            )
+        else:
+            d["deepgram"] = 'Not Found'
+        d["audio_path"] = str(d["audio_path"])
+        d["gcp_transcript"] = transcriber_gcp(gcp_seg.raw_data)
+        d["jasper_trained"] = transcriber_trained(jas_seg.raw_data)
+        d["jasper_all"] = transcriber_all_trained(jas_seg.raw_data)
+        d["jasper_libri"] = transcriber_libri_all_trained(jas_seg.raw_data)
+        return d
+
+    conv_data = ExtendedPath(conv_src).read_json()
+    conv_data["Dates"] = generate_dates()
+
+    dump_data_path = dump_dir / Path(data_name) / dump_file
+    ui_dump_data = ExtendedPath(dump_data_path).read_json()["data"]
+    ui_dump_map = {i["utterance_id"]: i for i in ui_dump_data}
+    manifest_path = dump_dir / Path(data_name) / manifest_file
+    test_points = list(asr_manifest_reader(manifest_path))
+    test_data_objs = [{**(ui_dump_map[t["audio_path"].stem]), **t} for t in test_points]
+    test_data = parallel_apply(process_data, test_data_objs)
+    # test_data = [process_data(t) for t in test_data_objs]
+    test_path = dump_dir / Path(data_name) / test_file
+
+    def dd_gen(dump_data):
+        for dd in dump_data:
+            ent = find_ent(dd, conv_data[extraction_key])
+            dd["entity"] = ent
+            if ent:
+                yield dd
+
+    asr_test_writer(test_path, dd_gen(test_data))
+    # for i, b in enumerate(batch(test_data, 1)):
+    #     test_fname = Path(f"{test_file.stem}_{i}.reg")
+    #     test_path = dump_dir / Path(data_name) / test_fname
+    #     asr_test_writer(test_path, dd_gen(test_data))
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()

jasper/data/tts/googletts.py

@@ -0,0 +1,52 @@
+from logging import getLogger
+from google.cloud import texttospeech
+
+LOGGER = getLogger("googletts")
+
+
+class GoogleTTS(object):
+    def __init__(self):
+        self.client = texttospeech.TextToSpeechClient()
+
+    def text_to_speech(self, text: str, params: dict) -> bytes:
+        tts_input = texttospeech.types.SynthesisInput(ssml=text)
+        voice = texttospeech.types.VoiceSelectionParams(
+            language_code=params["language"], name=params["name"]
+        )
+        audio_config = texttospeech.types.AudioConfig(
+            audio_encoding=texttospeech.enums.AudioEncoding.LINEAR16,
+            sample_rate_hertz=params["sample_rate"],
+        )
+        response = self.client.synthesize_speech(tts_input, voice, audio_config)
+        audio_content = response.audio_content
+        return audio_content
+
+    @classmethod
+    def voice_list(cls):
+        """Lists the available voices."""
+
+        client = cls().client
+
+        # Performs the list voices request
+        voices = client.list_voices()
+        results = []
+        for voice in voices.voices:
+            supported_eng_langs = [
+                lang for lang in voice.language_codes if lang[:2] == "en"
+            ]
+            if len(supported_eng_langs) > 0:
+                lang = ",".join(supported_eng_langs)
+            else:
+                continue
+
+            ssml_gender = texttospeech.enums.SsmlVoiceGender(voice.ssml_gender)
+            results.append(
+                {
+                    "name": voice.name,
+                    "language": lang,
+                    "gender": ssml_gender.name,
+                    "engine": "wavenet" if "Wav" in voice.name else "standard",
+                    "sample_rate": voice.natural_sample_rate_hertz,
+                }
+            )
+        return results

jasper/data/tts/ttsclient.py

@@ -0,0 +1,26 @@
+"""
+TTSClient Abstract Class
+"""
+from abc import ABC, abstractmethod
+
+
+class TTSClient(ABC):
+    """
+    Base class for TTS
+    """
+
+    @abstractmethod
+    def text_to_speech(self, text: str, num_channels: int, sample_rate: int,
+                       audio_encoding) -> bytes:
+        """
+        convert text to bytes
+
+        Arguments:
+            text {[type]} -- text to convert
+            channel {[type]} -- output audio bytes channel setting
+            width {[type]} -- width of audio bytes
+            rate {[type]} -- rare for audio bytes
+
+        Returns:
+            [type] -- [description]
+        """

jasper/data/tts_generator.py

@@ -0,0 +1,62 @@
+# import io
+# import sys
+# import json
+import argparse
+import logging
+from pathlib import Path
+from .utils import random_pnr_generator, asr_data_writer
+from .tts.googletts import GoogleTTS
+from tqdm import tqdm
+import random
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+def pnr_tts_streamer(count):
+    google_voices = GoogleTTS.voice_list()
+    gtts = GoogleTTS()
+    for pnr_code in tqdm(random_pnr_generator(count)):
+        tts_code = f'<speak><say-as interpret-as="verbatim">{pnr_code}</say-as></speak>'
+        param = random.choice(google_voices)
+        param["sample_rate"] = 24000
+        param["num_channels"] = 1
+        wav_data = gtts.text_to_speech(text=tts_code, params=param)
+        audio_dur = len(wav_data[44:]) / (2 * 24000)
+        yield pnr_code, audio_dur, wav_data
+
+
+def generate_asr_data_fromtts(output_dir, dataset_name, count):
+    asr_data_writer(output_dir, dataset_name, pnr_tts_streamer(count))
+
+
+def arg_parser():
+    prog = Path(__file__).stem
+    parser = argparse.ArgumentParser(
+        prog=prog, description=f"generates asr training data"
+    )
+    parser.add_argument(
+        "--output_dir",
+        type=Path,
+        default=Path("./train/asr_data"),
+        help="directory to output asr data",
+    )
+    parser.add_argument(
+        "--count", type=int, default=3, help="number of datapoints to generate"
+    )
+    parser.add_argument(
+        "--dataset_name", type=str, default="pnr_data", help="name of the dataset"
+    )
+    return parser
+
+
+def main():
+    parser = arg_parser()
+    args = parser.parse_args()
+    generate_asr_data_fromtts(**vars(args))
+
+
+if __name__ == "__main__":
+    main()

jasper/data/utils.py

@@ -0,0 +1,485 @@
+import io
+import os
+import json
+import wave
+from pathlib import Path
+from itertools import product
+from functools import partial
+from math import floor
+from uuid import uuid4
+from urllib.parse import urlsplit
+from concurrent.futures import ThreadPoolExecutor
+
+import numpy as np
+import pymongo
+from slugify import slugify
+from num2words import num2words
+from jasper.client import transcribe_gen
+from nemo.collections.asr.metrics import word_error_rate
+import matplotlib.pyplot as plt
+import librosa
+import librosa.display
+from tqdm import tqdm
+
+
+def manifest_str(path, dur, text):
+    return (
+        json.dumps({"audio_filepath": path, "duration": round(dur, 1), "text": text})
+        + "\n"
+    )
+
+
+def wav_bytes(audio_bytes, frame_rate=24000):
+    wf_b = io.BytesIO()
+    with wave.open(wf_b, mode="w") as wf:
+        wf.setnchannels(1)
+        wf.setframerate(frame_rate)
+        wf.setsampwidth(2)
+        wf.writeframesraw(audio_bytes)
+    return wf_b.getvalue()
+
+
+def random_pnr_generator(count=10000):
+    LENGTH = 3
+
+    # alphabet = list("abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ")
+    alphabet = list("ABCDEFGHIJKLMNOPQRSTUVWXYZ")
+    numeric = list("0123456789")
+    np_alphabet = np.array(alphabet, dtype="|S1")
+    np_numeric = np.array(numeric, dtype="|S1")
+    np_alpha_codes = np.random.choice(np_alphabet, [count, LENGTH])
+    np_num_codes = np.random.choice(np_numeric, [count, LENGTH])
+    np_code_seed = np.concatenate((np_alpha_codes, np_num_codes), axis=1).T
+    np.random.shuffle(np_code_seed)
+    np_codes = np_code_seed.T
+    codes = [(b"".join(np_codes[i])).decode("utf-8") for i in range(len(np_codes))]
+    return codes
+
+
+def alnum_to_asr_tokens(text):
+    letters = " ".join(list(text))
+    num_tokens = [num2words(c) if "0" <= c <= "9" else c for c in letters]
+    return ("".join(num_tokens)).lower()
+
+
+def tscript_uuid_fname(transcript):
+    return str(uuid4()) + "_" + slugify(transcript, max_length=8)
+
+
+def asr_data_writer(output_dir, dataset_name, asr_data_source, verbose=False):
+    dataset_dir = output_dir / Path(dataset_name)
+    (dataset_dir / Path("wav")).mkdir(parents=True, exist_ok=True)
+    asr_manifest = dataset_dir / Path("manifest.json")
+    num_datapoints = 0
+    with asr_manifest.open("w") as mf:
+        print(f"writing manifest to {asr_manifest}")
+        for transcript, audio_dur, wav_data in asr_data_source:
+            fname = tscript_uuid_fname(transcript)
+            audio_file = dataset_dir / Path("wav") / Path(fname).with_suffix(".wav")
+            audio_file.write_bytes(wav_data)
+            rel_pnr_path = audio_file.relative_to(dataset_dir)
+            manifest = manifest_str(str(rel_pnr_path), audio_dur, transcript)
+            mf.write(manifest)
+            if verbose:
+                print(f"writing '{transcript}' of duration {audio_dur}")
+            num_datapoints += 1
+    return num_datapoints
+
+
+def ui_dump_manifest_writer(output_dir, dataset_name, asr_data_source, verbose=False):
+    dataset_dir = output_dir / Path(dataset_name)
+    (dataset_dir / Path("wav")).mkdir(parents=True, exist_ok=True)
+    ui_dump_file = dataset_dir / Path("ui_dump.json")
+    (dataset_dir / Path("wav_plots")).mkdir(parents=True, exist_ok=True)
+    asr_manifest = dataset_dir / Path("manifest.json")
+    num_datapoints = 0
+    ui_dump = {
+        "use_domain_asr": False,
+        "annotation_only": False,
+        "enable_plots": True,
+        "data": [],
+    }
+    data_funcs = []
+    transcriber_gcp = gcp_transcribe_gen()
+    transcriber_pretrained = transcribe_gen(asr_port=8044)
+    with asr_manifest.open("w") as mf:
+        print(f"writing manifest to {asr_manifest}")
+
+        def data_fn(
+            transcript,
+            audio_dur,
+            wav_data,
+            caller_name,
+            aud_seg,
+            fname,
+            audio_path,
+            num_datapoints,
+            rel_pnr_path,
+        ):
+            pretrained_result = transcriber_pretrained(aud_seg.raw_data)
+            gcp_seg = aud_seg.set_frame_rate(16000)
+            gcp_result = transcriber_gcp(gcp_seg.raw_data)
+            pretrained_wer = word_error_rate([transcript], [pretrained_result])
+            wav_plot_path = (
+                dataset_dir / Path("wav_plots") / Path(fname).with_suffix(".png")
+            )
+            if not wav_plot_path.exists():
+                plot_seg(wav_plot_path, audio_path)
+            return {
+                "audio_filepath": str(rel_pnr_path),
+                "duration": round(audio_dur, 1),
+                "text": transcript,
+                "real_idx": num_datapoints,
+                "audio_path": audio_path,
+                "spoken": transcript,
+                "caller": caller_name,
+                "utterance_id": fname,
+                "gcp_asr": gcp_result,
+                "pretrained_asr": pretrained_result,
+                "pretrained_wer": pretrained_wer,
+                "plot_path": str(wav_plot_path),
+            }
+
+        for transcript, audio_dur, wav_data, caller_name, aud_seg in asr_data_source:
+            fname = str(uuid4()) + "_" + slugify(transcript, max_length=8)
+            audio_file = dataset_dir / Path("wav") / Path(fname).with_suffix(".wav")
+            audio_file.write_bytes(wav_data)
+            audio_path = str(audio_file)
+            rel_pnr_path = audio_file.relative_to(dataset_dir)
+            manifest = manifest_str(str(rel_pnr_path), audio_dur, transcript)
+            mf.write(manifest)
+            data_funcs.append(
+                partial(
+                    data_fn,
+                    transcript,
+                    audio_dur,
+                    wav_data,
+                    caller_name,
+                    aud_seg,
+                    fname,
+                    audio_path,
+                    num_datapoints,
+                    rel_pnr_path,
+                )
+            )
+            num_datapoints += 1
+    dump_data = parallel_apply(lambda x: x(), data_funcs)
+    # dump_data = [x() for x in tqdm(data_funcs)]
+    ui_dump["data"] = dump_data
+    ExtendedPath(ui_dump_file).write_json(ui_dump)
+    return num_datapoints
+
+
+def asr_manifest_reader(data_manifest_path: Path):
+    print(f"reading manifest from {data_manifest_path}")
+    with data_manifest_path.open("r") as pf:
+        pnr_jsonl = pf.readlines()
+    pnr_data = [json.loads(v) for v in pnr_jsonl]
+    for p in pnr_data:
+        p["audio_path"] = data_manifest_path.parent / Path(p["audio_filepath"])
+        p["text"] = p["text"].strip()
+        yield p
+
+
+def asr_manifest_writer(asr_manifest_path: Path, manifest_str_source):
+    with asr_manifest_path.open("w") as mf:
+        print(f"opening {asr_manifest_path} for writing manifest")
+        for mani_dict in manifest_str_source:
+            manifest = manifest_str(
+                mani_dict["audio_filepath"], mani_dict["duration"], mani_dict["text"]
+            )
+            mf.write(manifest)
+
+
+def asr_test_writer(out_file_path: Path, source):
+    def dd_str(dd, idx):
+        path = dd["audio_filepath"]
+        # dur = dd["duration"]
+        # return f"SAY {idx}\nPAUSE 3\nPLAY {path}\nPAUSE 3\n\n"
+        return f"PAUSE 2\nPLAY {path}\nPAUSE 60\n\n"
+
+    res_file = out_file_path.with_suffix(".result.json")
+    with out_file_path.open("w") as of:
+        print(f"opening {out_file_path} for writing test")
+        results = []
+        idx = 0
+        for ui_dd in source:
+            results.append(ui_dd)
+            out_str = dd_str(ui_dd, idx)
+            of.write(out_str)
+            idx += 1
+        of.write("DO_HANGUP\n")
+        ExtendedPath(res_file).write_json(results)
+
+
+def batch(iterable, n=1):
+    ls = len(iterable)
+    return [iterable[ndx : min(ndx + n, ls)] for ndx in range(0, ls, n)]
+
+
+class ExtendedPath(type(Path())):
+    """docstring for ExtendedPath."""
+
+    def read_json(self):
+        print(f"reading json from {self}")
+        with self.open("r") as jf:
+            return json.load(jf)
+
+    def write_json(self, data):
+        print(f"writing json to {self}")
+        self.parent.mkdir(parents=True, exist_ok=True)
+        with self.open("w") as jf:
+            return json.dump(data, jf, indent=2)
+
+
+def get_mongo_coll(uri="mongodb://localhost:27017/test.calls"):
+    ud = pymongo.uri_parser.parse_uri(uri)
+    conn = pymongo.MongoClient(uri)
+    return conn[ud["database"]][ud["collection"]]
+
+
+def get_mongo_conn(host="", port=27017, db="test", col="calls"):
+    mongo_host = host if host else os.environ.get("MONGO_HOST", "localhost")
+    mongo_uri = f"mongodb://{mongo_host}:{port}/"
+    return pymongo.MongoClient(mongo_uri)[db][col]
+
+
+def strip_silence(sound):
+    from pydub.silence import detect_leading_silence
+
+    start_trim = detect_leading_silence(sound)
+    end_trim = detect_leading_silence(sound.reverse())
+    duration = len(sound)
+    return sound[start_trim : duration - end_trim]
+
+
+def plot_seg(wav_plot_path, audio_path):
+    fig = plt.Figure()
+    ax = fig.add_subplot()
+    (y, sr) = librosa.load(audio_path)
+    librosa.display.waveplot(y=y, sr=sr, ax=ax)
+    with wav_plot_path.open("wb") as wav_plot_f:
+        fig.set_tight_layout(True)
+        fig.savefig(wav_plot_f, format="png", dpi=50)
+
+
+def generate_dates():
+
+    days = [i for i in range(1, 32)]
+    months = [
+        "January",
+        "February",
+        "March",
+        "April",
+        "May",
+        "June",
+        "July",
+        "August",
+        "September",
+        "October",
+        "November",
+        "December",
+    ]
+    # ordinal from https://stackoverflow.com/questions/9647202/ordinal-numbers-replacement
+
+    def ordinal(n):
+        return "%d%s" % (
+            n,
+            "tsnrhtdd"[(floor(n / 10) % 10 != 1) * (n % 10 < 4) * n % 10 :: 4],
+        )
+
+    def canon_vars(d, m):
+        return [
+            ordinal(d) + " " + m,
+            m + " " + ordinal(d),
+            ordinal(d) + " of " + m,
+            m + " the " + ordinal(d),
+            str(d) + " " + m,
+            m + " " + str(d),
+        ]
+
+    return [dm for d, m in product(days, months) for dm in canon_vars(d, m)]
+
+
+def get_call_logs(call_obj, s3, call_meta_dir):
+    meta_s3_uri = call_obj["DataURI"]
+    s3_event_url_p = urlsplit(meta_s3_uri)
+    saved_meta_path = call_meta_dir / Path(Path(s3_event_url_p.path).name)
+    if not saved_meta_path.exists():
+        print(f"downloading : {saved_meta_path} from {meta_s3_uri}")
+        s3.download_file(
+            s3_event_url_p.netloc, s3_event_url_p.path[1:], str(saved_meta_path)
+        )
+    call_metas = json.load(saved_meta_path.open())
+    return call_metas
+
+
+def gcp_transcribe_gen():
+    from google.cloud import speech_v1
+    from google.cloud.speech_v1 import enums
+
+    # import io
+    client = speech_v1.SpeechClient()
+    # local_file_path = 'resources/brooklyn_bridge.raw'
+
+    # The language of the supplied audio
+    language_code = "en-US"
+    model = "phone_call"
+
+    # Sample rate in Hertz of the audio data sent
+    sample_rate_hertz = 16000
+
+    # Encoding of audio data sent. This sample sets this explicitly.
+    # This field is optional for FLAC and WAV audio formats.
+    encoding = enums.RecognitionConfig.AudioEncoding.LINEAR16
+    config = {
+        "language_code": language_code,
+        "sample_rate_hertz": sample_rate_hertz,
+        "encoding": encoding,
+        "model": model,
+        "enable_automatic_punctuation": True,
+        "max_alternatives": 10,
+        "enable_word_time_offsets": True,  # used to detect start and end time of utterances
+        "speech_contexts": [
+            {
+                "phrases": [
+                    "$OOV_CLASS_ALPHANUMERIC_SEQUENCE",
+                    "$OOV_CLASS_DIGIT_SEQUENCE",
+                    "$TIME",
+                    "$YEAR",
+                ]
+            },
+            {
+                "phrases": [
+                    "A",
+                    "B",
+                    "C",
+                    "D",
+                    "E",
+                    "F",
+                    "G",
+                    "H",
+                    "I",
+                    "J",
+                    "K",
+                    "L",
+                    "M",
+                    "N",
+                    "O",
+                    "P",
+                    "Q",
+                    "R",
+                    "S",
+                    "T",
+                    "U",
+                    "V",
+                    "W",
+                    "X",
+                    "Y",
+                    "Z",
+                ]
+            },
+            {
+                "phrases": [
+                    "PNR is $OOV_CLASS_ALPHANUMERIC_SEQUENCE",
+                    "my PNR is $OOV_CLASS_ALPHANUMERIC_SEQUENCE",
+                    "my PNR number is $OOV_CLASS_ALPHANUMERIC_SEQUENCE",
+                    "PNR number is $OOV_CLASS_ALPHANUMERIC_SEQUENCE",
+                    "It's $OOV_CLASS_ALPHANUMERIC_SEQUENCE",
+                    "$OOV_CLASS_ALPHANUMERIC_SEQUENCE is my PNR",
+                ]
+            },
+            {"phrases": ["my name is"]},
+            {"phrases": ["Number $ORDINAL", "Numeral $ORDINAL"]},
+            {
+                "phrases": [
+                    "John Smith",
+                    "Carina Hu",
+                    "Travis Lim",
+                    "Marvin Tan",
+                    "Samuel Tan",
+                    "Dawn Mathew",
+                    "Dawn",
+                    "Mathew",
+                ]
+            },
+            {
+                "phrases": [
+                    "Beijing",
+                    "Tokyo",
+                    "London",
+                    "19 August",
+                    "7 October",
+                    "11 December",
+                    "17 September",
+                    "19th August",
+                    "7th October",
+                    "11th December",
+                    "17th September",
+                    "ABC123",
+                    "KWXUNP",
+                    "XLU5K1",
+                    "WL2JV6",
+                    "KBS651",
+                ]
+            },
+            {
+                "phrases": [
+                    "first flight",
+                    "second flight",
+                    "third flight",
+                    "first option",
+                    "second option",
+                    "third option",
+                    "first one",
+                    "second one",
+                    "third one",
+                ]
+            },
+        ],
+        "metadata": {
+            "industry_naics_code_of_audio": 481111,
+            "interaction_type": enums.RecognitionMetadata.InteractionType.PHONE_CALL,
+        },
+    }
+
+    def sample_recognize(content):
+        """
+        Transcribe a short audio file using synchronous speech recognition
+
+        Args:
+          local_file_path Path to local audio file, e.g. /path/audio.wav
+        """
+
+        # with io.open(local_file_path, "rb") as f:
+        #     content = f.read()
+        audio = {"content": content}
+
+        response = client.recognize(config, audio)
+        for result in response.results:
+            # First alternative is the most probable result
+            return "/".join([alt.transcript for alt in result.alternatives])
+            # print(u"Transcript: {}".format(alternative.transcript))
+        return ""
+
+    return sample_recognize
+
+
+def parallel_apply(fn, iterable, workers=8):
+    with ThreadPoolExecutor(max_workers=workers) as exe:
+        print(f"parallelly applying {fn}")
+        return [
+            res
+            for res in tqdm(
+                exe.map(fn, iterable), position=0, leave=True, total=len(iterable)
+            )
+        ]
+
+
+def main():
+    for c in random_pnr_generator():
+        print(c)
+
+
+if __name__ == "__main__":
+    main()

jasper/data/validation/__init__.py

@@ -0,0 +1 @@
+

jasper/data/validation/process.py

@@ -0,0 +1,425 @@
+import json
+import shutil
+from pathlib import Path
+from enum import Enum
+
+import typer
+from tqdm import tqdm
+
+from ..utils import (
+    alnum_to_asr_tokens,
+    ExtendedPath,
+    asr_manifest_reader,
+    asr_manifest_writer,
+    tscript_uuid_fname,
+    get_mongo_conn,
+    plot_seg,
+)
+
+app = typer.Typer()
+
+
+def preprocess_datapoint(
+    idx, rel_root, sample, use_domain_asr, annotation_only, enable_plots
+):
+    from pydub import AudioSegment
+    from nemo.collections.asr.metrics import word_error_rate
+    from jasper.client import transcribe_gen
+
+    try:
+        res = dict(sample)
+        res["real_idx"] = idx
+        audio_path = rel_root / Path(sample["audio_filepath"])
+        res["audio_path"] = str(audio_path)
+        if use_domain_asr:
+            res["spoken"] = alnum_to_asr_tokens(res["text"])
+        else:
+            res["spoken"] = res["text"]
+        res["utterance_id"] = audio_path.stem
+        if not annotation_only:
+            transcriber_pretrained = transcribe_gen(asr_port=8044)
+
+            aud_seg = (
+                AudioSegment.from_file_using_temporary_files(audio_path)
+                .set_channels(1)
+                .set_sample_width(2)
+                .set_frame_rate(24000)
+            )
+            res["pretrained_asr"] = transcriber_pretrained(aud_seg.raw_data)
+            res["pretrained_wer"] = word_error_rate(
+                [res["text"]], [res["pretrained_asr"]]
+            )
+            if use_domain_asr:
+                transcriber_speller = transcribe_gen(asr_port=8045)
+                res["domain_asr"] = transcriber_speller(aud_seg.raw_data)
+                res["domain_wer"] = word_error_rate(
+                    [res["spoken"]], [res["pretrained_asr"]]
+                )
+        if enable_plots:
+            wav_plot_path = (
+                rel_root / Path("wav_plots") / Path(audio_path.name).with_suffix(".png")
+            )
+            if not wav_plot_path.exists():
+                plot_seg(wav_plot_path, audio_path)
+            res["plot_path"] = str(wav_plot_path)
+        return res
+    except BaseException as e:
+        print(f'failed on {idx}: {sample["audio_filepath"]} with {e}')
+
+
+@app.command()
+def dump_ui(
+    data_name: str = typer.Option("call_alphanum", show_default=True),
+    dataset_dir: Path = Path("./data/asr_data"),
+    dump_dir: Path = Path("./data/valiation_data"),
+    dump_fname: Path = typer.Option(Path("ui_dump.json"), show_default=True),
+    use_domain_asr: bool = False,
+    annotation_only: bool = False,
+    enable_plots: bool = True,
+):
+    from concurrent.futures import ThreadPoolExecutor
+    from functools import partial
+
+    data_manifest_path = dataset_dir / Path(data_name) / Path("manifest.json")
+    dump_path: Path = dump_dir / Path(data_name) / dump_fname
+    plot_dir = data_manifest_path.parent / Path("wav_plots")
+    plot_dir.mkdir(parents=True, exist_ok=True)
+    typer.echo(f"Using data manifest:{data_manifest_path}")
+    with data_manifest_path.open("r") as pf:
+        pnr_jsonl = pf.readlines()
+        pnr_funcs = [
+            partial(
+                preprocess_datapoint,
+                i,
+                data_manifest_path.parent,
+                json.loads(v),
+                use_domain_asr,
+                annotation_only,
+                enable_plots,
+            )
+            for i, v in enumerate(pnr_jsonl)
+        ]
+
+        def exec_func(f):
+            return f()
+
+        with ThreadPoolExecutor() as exe:
+            print("starting all preprocess tasks")
+            pnr_data = filter(
+                None,
+                list(
+                    tqdm(
+                        exe.map(exec_func, pnr_funcs),
+                        position=0,
+                        leave=True,
+                        total=len(pnr_funcs),
+                    )
+                ),
+            )
+    if annotation_only:
+        result = list(pnr_data)
+    else:
+        wer_key = "domain_wer" if use_domain_asr else "pretrained_wer"
+        result = sorted(pnr_data, key=lambda x: x[wer_key], reverse=True)
+    ui_config = {
+        "use_domain_asr": use_domain_asr,
+        "annotation_only": annotation_only,
+        "enable_plots": enable_plots,
+        "data": result,
+    }
+    ExtendedPath(dump_path).write_json(ui_config)
+
+
+@app.command()
+def sample_ui(
+    data_name: str = typer.Option("call_upwork_train_cnd", show_default=True),
+    dump_dir: Path = Path("./data/asr_data"),
+    dump_file: Path = Path("ui_dump.json"),
+    sample_count: int = typer.Option(80, show_default=True),
+    sample_file: Path = Path("sample_dump.json"),
+):
+    import pandas as pd
+
+    processed_data_path = dump_dir / Path(data_name) / dump_file
+    sample_path = dump_dir / Path(data_name) / sample_file
+    processed_data = ExtendedPath(processed_data_path).read_json()
+    df = pd.DataFrame(processed_data["data"])
+    samples_per_caller = sample_count // len(df["caller"].unique())
+    caller_samples = pd.concat(
+        [g.sample(samples_per_caller) for (c, g) in df.groupby("caller")]
+    )
+    caller_samples = caller_samples.reset_index(drop=True)
+    caller_samples["real_idx"] = caller_samples.index
+    sample_data = caller_samples.to_dict("records")
+    processed_data["data"] = sample_data
+    typer.echo(f"sampling {sample_count} datapoints")
+    ExtendedPath(sample_path).write_json(processed_data)
+
+
+@app.command()
+def task_ui(
+    data_name: str = typer.Option("call_upwork_train_cnd", show_default=True),
+    dump_dir: Path = Path("./data/asr_data"),
+    dump_file: Path = Path("ui_dump.json"),
+    task_count: int = typer.Option(4, show_default=True),
+    task_file: str = "task_dump",
+):
+    import pandas as pd
+    import numpy as np
+
+    processed_data_path = dump_dir / Path(data_name) / dump_file
+    processed_data = ExtendedPath(processed_data_path).read_json()
+    df = pd.DataFrame(processed_data["data"]).sample(frac=1).reset_index(drop=True)
+    for t_idx, task_f in enumerate(np.array_split(df, task_count)):
+        task_f = task_f.reset_index(drop=True)
+        task_f["real_idx"] = task_f.index
+        task_data = task_f.to_dict("records")
+        processed_data["data"] = task_data
+        task_path = dump_dir / Path(data_name) / Path(task_file + f"-{t_idx}.json")
+        ExtendedPath(task_path).write_json(processed_data)
+
+
+@app.command()
+def dump_corrections(
+    task_uid: str,
+    data_name: str = typer.Option("call_alphanum", show_default=True),
+    dump_dir: Path = Path("./data/asr_data"),
+    dump_fname: Path = Path("corrections.json"),
+):
+    dump_path = dump_dir / Path(data_name) / dump_fname
+    col = get_mongo_conn(col="asr_validation")
+    task_id = [c for c in col.distinct("task_id") if c.rsplit("-", 1)[1] == task_uid][0]
+    corrections = list(col.find({"type": "correction"}, projection={"_id": False}))
+    cursor_obj = col.find({"type": "correction", "task_id": task_id}, projection={"_id": False})
+    corrections = [c for c in cursor_obj]
+    ExtendedPath(dump_path).write_json(corrections)
+
+
+@app.command()
+def caller_quality(
+    task_uid: str,
+    data_name: str = typer.Option("call_upwork_train_cnd", show_default=True),
+    dump_dir: Path = Path("./data/asr_data"),
+    dump_fname: Path = Path("ui_dump.json"),
+    correction_fname: Path = Path("corrections.json"),
+):
+    import copy
+    import pandas as pd
+
+    dump_path = dump_dir / Path(data_name) / dump_fname
+    correction_path = dump_dir / Path(data_name) / correction_fname
+    dump_data = ExtendedPath(dump_path).read_json()
+
+    dump_map = {d["utterance_id"]: d for d in dump_data["data"]}
+    correction_data = ExtendedPath(correction_path).read_json()
+
+    def correction_dp(c):
+        dp = copy.deepcopy(dump_map[c["code"]])
+        dp["valid"] = c["value"]["status"] == "Correct"
+        return dp
+
+    corrected_dump = [
+        correction_dp(c)
+        for c in correction_data
+        if c["task_id"].rsplit("-", 1)[1] == task_uid
+    ]
+    df = pd.DataFrame(corrected_dump)
+    print(f"Total samples: {len(df)}")
+    for (c, g) in df.groupby("caller"):
+        total = len(g)
+        valid = len(g[g["valid"] == True])
+        valid_rate = valid * 100 / total
+        print(f"Caller: {c} Valid%:{valid_rate:.2f} of {total} samples")
+
+
+@app.command()
+def fill_unannotated(
+    data_name: str = typer.Option("call_alphanum", show_default=True),
+    dump_dir: Path = Path("./data/valiation_data"),
+    dump_file: Path = Path("ui_dump.json"),
+    corrections_file: Path = Path("corrections.json"),
+):
+    processed_data_path = dump_dir / Path(data_name) / dump_file
+    corrections_path = dump_dir / Path(data_name) / corrections_file
+    processed_data = json.load(processed_data_path.open())
+    corrections = json.load(corrections_path.open())
+    annotated_codes = {c["code"] for c in corrections}
+    all_codes = {c["gold_chars"] for c in processed_data}
+    unann_codes = all_codes - annotated_codes
+    mongo_conn = get_mongo_conn(col="asr_validation")
+    for c in unann_codes:
+        mongo_conn.find_one_and_update(
+            {"type": "correction", "code": c},
+            {"$set": {"value": {"status": "Inaudible", "correction": ""}}},
+            upsert=True,
+        )
+
+
+class ExtractionType(str, Enum):
+    date = "dates"
+    city = "cities"
+    name = "names"
+    all = "all"
+
+
+@app.command()
+def split_extract(
+    data_name: str = typer.Option("call_alphanum", show_default=True),
+    # dest_data_name: str = typer.Option("call_aldata_namephanum_date", show_default=True),
+    # dump_dir: Path = Path("./data/valiation_data"),
+    dump_dir: Path = Path("./data/asr_data"),
+    dump_file: Path = Path("ui_dump.json"),
+    manifest_file: Path = Path("manifest.json"),
+    corrections_file: str = typer.Option("corrections.json", show_default=True),
+    conv_data_path: Path = typer.Option(Path("./data/conv_data.json"), show_default=True),
+    extraction_type: ExtractionType = ExtractionType.all,
+):
+    import shutil
+
+    data_manifest_path = dump_dir / Path(data_name) / manifest_file
+    conv_data = ExtendedPath(conv_data_path).read_json()
+
+    def extract_data_of_type(extraction_key):
+        extraction_vals = conv_data[extraction_key]
+        dest_data_name = data_name + "_" + extraction_key.lower()
+
+        manifest_gen = asr_manifest_reader(data_manifest_path)
+        dest_data_dir = dump_dir / Path(dest_data_name)
+        dest_data_dir.mkdir(exist_ok=True, parents=True)
+        (dest_data_dir / Path("wav")).mkdir(exist_ok=True, parents=True)
+        dest_manifest_path = dest_data_dir / manifest_file
+        dest_ui_path = dest_data_dir / dump_file
+
+        def extract_manifest(mg):
+            for m in mg:
+                if m["text"] in extraction_vals:
+                    shutil.copy(m["audio_path"], dest_data_dir / Path(m["audio_filepath"]))
+                    yield m
+
+        asr_manifest_writer(dest_manifest_path, extract_manifest(manifest_gen))
+
+        ui_data_path = dump_dir / Path(data_name) / dump_file
+        orig_ui_data = ExtendedPath(ui_data_path).read_json()
+        ui_data = orig_ui_data["data"]
+        file_ui_map = {Path(u["audio_filepath"]).stem: u for u in ui_data}
+        extracted_ui_data = list(filter(lambda u: u["text"] in extraction_vals, ui_data))
+        final_data = []
+        for i, d in enumerate(extracted_ui_data):
+            d['real_idx'] = i
+            final_data.append(d)
+        orig_ui_data['data'] = final_data
+        ExtendedPath(dest_ui_path).write_json(orig_ui_data)
+
+        if corrections_file:
+            dest_correction_path = dest_data_dir / corrections_file
+            corrections_path = dump_dir / Path(data_name) / corrections_file
+            corrections = json.load(corrections_path.open())
+            extracted_corrections = list(
+                filter(
+                    lambda c: c["code"] in file_ui_map
+                    and file_ui_map[c["code"]]["text"] in extraction_vals,
+                    corrections,
+                )
+            )
+            ExtendedPath(dest_correction_path).write_json(extracted_corrections)
+
+    if extraction_type.value == 'all':
+        for ext_key in conv_data.keys():
+            extract_data_of_type(ext_key)
+    else:
+        extract_data_of_type(extraction_type.value)
+
+
+@app.command()
+def update_corrections(
+    data_name: str = typer.Option("call_alphanum", show_default=True),
+    dump_dir: Path = Path("./data/asr_data"),
+    manifest_file: Path = Path("manifest.json"),
+    corrections_file: Path = Path("corrections.json"),
+    ui_dump_file: Path = Path("ui_dump.json"),
+    skip_incorrect: bool = typer.Option(True, show_default=True),
+):
+    data_manifest_path = dump_dir / Path(data_name) / manifest_file
+    corrections_path = dump_dir / Path(data_name) / corrections_file
+    ui_dump_path = dump_dir / Path(data_name) / ui_dump_file
+
+    def correct_manifest(ui_dump_path, corrections_path):
+        corrections = ExtendedPath(corrections_path).read_json()
+        ui_data = ExtendedPath(ui_dump_path).read_json()['data']
+        correct_set = {
+            c["code"] for c in corrections if c["value"]["status"] == "Correct"
+        }
+        # incorrect_set = {c["code"] for c in corrections if c["value"]["status"] == "Inaudible"}
+        correction_map = {
+            c["code"]: c["value"]["correction"]
+            for c in corrections
+            if c["value"]["status"] == "Incorrect"
+        }
+        # for d in manifest_data_gen:
+        #     if d["chars"] in incorrect_set:
+        #         d["audio_path"].unlink()
+        # renamed_set = set()
+        for d in ui_data:
+            if d["utterance_id"] in correct_set:
+                yield {
+                    "audio_filepath": d["audio_filepath"],
+                    "duration": d["duration"],
+                    "text": d["text"],
+                }
+            elif d["utterance_id"] in correction_map:
+                correct_text = correction_map[d["utterance_id"]]
+                if skip_incorrect:
+                    print(
+                        f'skipping incorrect {d["audio_path"]} corrected to {correct_text}'
+                    )
+                else:
+                    orig_audio_path = Path(d["audio_path"])
+                    new_name = str(Path(tscript_uuid_fname(correct_text)).with_suffix(".wav"))
+                    new_audio_path = orig_audio_path.with_name(new_name)
+                    orig_audio_path.replace(new_audio_path)
+                    new_filepath = str(Path(d["audio_filepath"]).with_name(new_name))
+                    yield {
+                        "audio_filepath": new_filepath,
+                        "duration": d["duration"],
+                        "text": correct_text,
+                    }
+            else:
+                orig_audio_path = Path(d["audio_path"])
+                # don't delete if another correction points to an old file
+                # if d["text"] not in renamed_set:
+                orig_audio_path.unlink()
+                # else:
+                #     print(f'skipping deletion of correction:{d["text"]}')
+
+    typer.echo(f"Using data manifest:{data_manifest_path}")
+    dataset_dir = data_manifest_path.parent
+    dataset_name = dataset_dir.name
+    backup_dir = dataset_dir.with_name(dataset_name + ".bkp")
+    if not backup_dir.exists():
+        typer.echo(f"backing up to :{backup_dir}")
+        shutil.copytree(str(dataset_dir), str(backup_dir))
+    # manifest_gen = asr_manifest_reader(data_manifest_path)
+    corrected_manifest = correct_manifest(ui_dump_path, corrections_path)
+    new_data_manifest_path = data_manifest_path.with_name("manifest.new")
+    asr_manifest_writer(new_data_manifest_path, corrected_manifest)
+    new_data_manifest_path.replace(data_manifest_path)
+
+
+@app.command()
+def clear_mongo_corrections():
+    delete = typer.confirm("are you sure you want to clear mongo collection it?")
+    if delete:
+        col = get_mongo_conn(col="asr_validation")
+        col.delete_many({"type": "correction"})
+        col.delete_many({"type": "current_cursor"})
+        typer.echo("deleted mongo collection.")
+        return
+    typer.echo("Aborted")
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()

jasper/data/validation/st_rerun.py

@@ -0,0 +1,38 @@
+import streamlit.ReportThread as ReportThread
+from streamlit.ScriptRequestQueue import RerunData
+from streamlit.ScriptRunner import RerunException
+from streamlit.server.Server import Server
+
+
+def rerun():
+    """Rerun a Streamlit app from the top!"""
+    widget_states = _get_widget_states()
+    raise RerunException(RerunData(widget_states))
+
+
+def _get_widget_states():
+    # Hack to get the session object from Streamlit.
+
+    ctx = ReportThread.get_report_ctx()
+
+    session = None
+
+    current_server = Server.get_current()
+    if hasattr(current_server, '_session_infos'):
+        # Streamlit < 0.56
+        session_infos = Server.get_current()._session_infos.values()
+    else:
+        session_infos = Server.get_current()._session_info_by_id.values()
+
+    for session_info in session_infos:
+        if session_info.session.enqueue == ctx.enqueue:
+            session = session_info.session
+
+    if session is None:
+        raise RuntimeError(
+            "Oh noes. Couldn't get your Streamlit Session object"
+            "Are you doing something fancy with threads?"
+        )
+    # Got the session object!
+
+    return session._widget_states

jasper/data/validation/ui.py

@@ -0,0 +1,158 @@
+from pathlib import Path
+
+import streamlit as st
+import typer
+from uuid import uuid4
+from ..utils import ExtendedPath, get_mongo_conn
+from .st_rerun import rerun
+
+app = typer.Typer()
+
+
+if not hasattr(st, "mongo_connected"):
+    st.mongoclient = get_mongo_conn(col="asr_validation")
+    mongo_conn = st.mongoclient
+    st.task_id = str(uuid4())
+
+    def current_cursor_fn():
+        # mongo_conn = st.mongoclient
+        cursor_obj = mongo_conn.find_one(
+            {"type": "current_cursor", "task_id": st.task_id}
+        )
+        cursor_val = cursor_obj["cursor"]
+        return cursor_val
+
+    def update_cursor_fn(val=0):
+        mongo_conn.find_one_and_update(
+            {"type": "current_cursor", "task_id": st.task_id},
+            {"$set": {"type": "current_cursor", "task_id": st.task_id, "cursor": val}},
+            upsert=True,
+        )
+        rerun()
+
+    def get_correction_entry_fn(code):
+        return mongo_conn.find_one(
+            {"type": "correction", "code": code}, projection={"_id": False}
+        )
+
+    def update_entry_fn(code, value):
+        mongo_conn.find_one_and_update(
+            {"type": "correction", "code": code},
+            {"$set": {"value": value, "task_id": st.task_id}},
+            upsert=True,
+        )
+
+    def set_task_fn(mf_path):
+        task_path = mf_path.parent / Path(f"task-{st.task_id}.lck")
+        if not task_path.exists():
+            print(f"creating task lock at {task_path}")
+            task_path.touch()
+
+    st.get_current_cursor = current_cursor_fn
+    st.update_cursor = update_cursor_fn
+    st.get_correction_entry = get_correction_entry_fn
+    st.update_entry = update_entry_fn
+    st.set_task = set_task_fn
+    st.mongo_connected = True
+    cursor_obj = mongo_conn.find_one({"type": "current_cursor", "task_id": st.task_id})
+    if not cursor_obj:
+        update_cursor_fn(0)
+
+
+@st.cache()
+def load_ui_data(validation_ui_data_path: Path):
+    typer.echo(f"Using validation ui data from {validation_ui_data_path}")
+    return ExtendedPath(validation_ui_data_path).read_json()
+
+
+@app.command()
+def main(manifest: Path):
+    st.set_task(manifest)
+    ui_config = load_ui_data(manifest)
+    asr_data = ui_config["data"]
+    use_domain_asr = ui_config.get("use_domain_asr", True)
+    annotation_only = ui_config.get("annotation_only", False)
+    enable_plots = ui_config.get("enable_plots", True)
+    sample_no = st.get_current_cursor()
+    if len(asr_data) - 1 < sample_no or sample_no < 0:
+        print("Invalid samplno resetting to 0")
+        st.update_cursor(0)
+    sample = asr_data[sample_no]
+    title_type = "Speller " if use_domain_asr else ""
+    task_uid = st.task_id.rsplit("-", 1)[1]
+    if annotation_only:
+        st.title(f"ASR Annotation - # {task_uid}")
+    else:
+        st.title(f"ASR {title_type}Validation - # {task_uid}")
+    addl_text = f"spelled *{sample['spoken']}*" if use_domain_asr else ""
+    st.markdown(f"{sample_no+1} of {len(asr_data)} : **{sample['text']}**" + addl_text)
+    new_sample = st.number_input(
+        "Go To Sample:", value=sample_no + 1, min_value=1, max_value=len(asr_data)
+    )
+    if new_sample != sample_no + 1:
+        st.update_cursor(new_sample - 1)
+    st.sidebar.title(f"Details: [{sample['real_idx']}]")
+    st.sidebar.markdown(f"Gold Text: **{sample['text']}**")
+    if not annotation_only:
+        if use_domain_asr:
+            st.sidebar.markdown(f"Expected Spelled: *{sample['spoken']}*")
+        st.sidebar.title("Results:")
+        st.sidebar.markdown(f"Pretrained: **{sample['pretrained_asr']}**")
+        if "caller" in sample:
+            st.sidebar.markdown(f"Caller: **{sample['caller']}**")
+        if use_domain_asr:
+            st.sidebar.markdown(f"Domain: **{sample['domain_asr']}**")
+            st.sidebar.title(f"Speller WER: {sample['domain_wer']:.2f}%")
+        else:
+            st.sidebar.title(f"Pretrained WER: {sample['pretrained_wer']:.2f}%")
+    if enable_plots:
+        st.sidebar.image(Path(sample["plot_path"]).read_bytes())
+    st.audio(Path(sample["audio_path"]).open("rb"))
+    # set default to text
+    corrected = sample["text"]
+    correction_entry = st.get_correction_entry(sample["utterance_id"])
+    selected_idx = 0
+    options = ("Correct", "Incorrect", "Inaudible")
+    # if correction entry is present set the corresponding ui defaults
+    if correction_entry:
+        selected_idx = options.index(correction_entry["value"]["status"])
+        corrected = correction_entry["value"]["correction"]
+    selected = st.radio("The Audio is", options, index=selected_idx)
+    if selected == "Incorrect":
+        corrected = st.text_input("Actual:", value=corrected)
+    if selected == "Inaudible":
+        corrected = ""
+    if st.button("Submit"):
+        st.update_entry(
+            sample["utterance_id"], {"status": selected, "correction": corrected}
+        )
+        st.update_cursor(sample_no + 1)
+    if correction_entry:
+        st.markdown(
+            f'Your Response: **{correction_entry["value"]["status"]}** Correction: **{correction_entry["value"]["correction"]}**'
+        )
+    text_sample = st.text_input("Go to Text:", value="")
+    if text_sample != "":
+        candidates = [
+            i
+            for (i, p) in enumerate(asr_data)
+            if p["text"] == text_sample or p["spoken"] == text_sample
+        ]
+        if len(candidates) > 0:
+            st.update_cursor(candidates[0])
+    real_idx = st.number_input(
+        "Go to real-index",
+        value=sample["real_idx"],
+        min_value=0,
+        max_value=len(asr_data) - 1,
+    )
+    if real_idx != int(sample["real_idx"]):
+        idx = [i for (i, p) in enumerate(asr_data) if p["real_idx"] == real_idx][0]
+        st.update_cursor(idx)
+
+
+if __name__ == "__main__":
+    try:
+        app()
+    except SystemExit:
+        pass

jasper/evaluate.py

@@ -0,0 +1,359 @@
+# Copyright (c) 2019 NVIDIA Corporation
+import argparse
+import copy
+# import math
+import os
+from pathlib import Path
+from functools import partial
+
+from ruamel.yaml import YAML
+
+import nemo
+import nemo.collections.asr as nemo_asr
+import nemo.utils.argparse as nm_argparse
+from nemo.collections.asr.helpers import (
+    # monitor_asr_train_progress,
+    process_evaluation_batch,
+    process_evaluation_epoch,
+)
+
+# from nemo.utils.lr_policies import CosineAnnealing
+from training.data_loaders import RpycAudioToTextDataLayer
+
+logging = nemo.logging
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        parents=[nm_argparse.NemoArgParser()],
+        description="Jasper",
+        conflict_handler="resolve",
+    )
+    parser.set_defaults(
+        checkpoint_dir=None,
+        optimizer="novograd",
+        batch_size=64,
+        eval_batch_size=64,
+        lr=0.002,
+        amp_opt_level="O1",
+        create_tb_writer=True,
+        model_config="./train/jasper10x5dr.yaml",
+        work_dir="./train/work",
+        num_epochs=300,
+        weight_decay=0.005,
+        checkpoint_save_freq=100,
+        eval_freq=100,
+        load_dir="./train/models/jasper/",
+        warmup_steps=3,
+        exp_name="jasper-speller",
+    )
+
+    # Overwrite default args
+    parser.add_argument(
+        "--max_steps",
+        type=int,
+        default=None,
+        required=False,
+        help="max number of steps to train",
+    )
+    parser.add_argument(
+        "--num_epochs", type=int, required=False, help="number of epochs to train"
+    )
+    parser.add_argument(
+        "--model_config",
+        type=str,
+        required=False,
+        help="model configuration file: model.yaml",
+    )
+    parser.add_argument(
+        "--encoder_checkpoint",
+        type=str,
+        required=True,
+        help="encoder checkpoint file: JasperEncoder.pt",
+    )
+    parser.add_argument(
+        "--decoder_checkpoint",
+        type=str,
+        required=True,
+        help="decoder checkpoint file: JasperDecoderForCTC.pt",
+    )
+    parser.add_argument(
+        "--remote_data",
+        type=str,
+        required=False,
+        default="",
+        help="remote dataloader endpoint",
+    )
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        required=False,
+        default="",
+        help="dataset directory containing train/test manifests",
+    )
+
+    # Create new args
+    parser.add_argument("--exp_name", default="Jasper", type=str)
+    parser.add_argument("--beta1", default=0.95, type=float)
+    parser.add_argument("--beta2", default=0.25, type=float)
+    parser.add_argument("--warmup_steps", default=0, type=int)
+    parser.add_argument(
+        "--load_dir",
+        default=None,
+        type=str,
+        help="directory with pre-trained checkpoint",
+    )
+
+    args = parser.parse_args()
+    if args.max_steps is None and args.num_epochs is None:
+        raise ValueError("Either max_steps or num_epochs should be provided.")
+    return args
+
+
+def construct_name(
+    name, lr, batch_size, max_steps, num_epochs, wd, optimizer, iter_per_step
+):
+    if max_steps is not None:
+        return "{0}-lr_{1}-bs_{2}-s_{3}-wd_{4}-opt_{5}-ips_{6}".format(
+            name, lr, batch_size, max_steps, wd, optimizer, iter_per_step
+        )
+    else:
+        return "{0}-lr_{1}-bs_{2}-e_{3}-wd_{4}-opt_{5}-ips_{6}".format(
+            name, lr, batch_size, num_epochs, wd, optimizer, iter_per_step
+        )
+
+
+def create_all_dags(args, neural_factory):
+    yaml = YAML(typ="safe")
+    with open(args.model_config) as f:
+        jasper_params = yaml.load(f)
+    vocab = jasper_params["labels"]
+    sample_rate = jasper_params["sample_rate"]
+
+    # Calculate num_workers for dataloader
+    total_cpus = os.cpu_count()
+    cpu_per_traindl = max(int(total_cpus / neural_factory.world_size), 1)
+    # perturb_config = jasper_params.get('perturb', None)
+    train_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"])
+    train_dl_params.update(jasper_params["AudioToTextDataLayer"]["train"])
+    del train_dl_params["train"]
+    del train_dl_params["eval"]
+    # del train_dl_params["normalize_transcripts"]
+
+    if args.dataset:
+        d_path = Path(args.dataset)
+        if not args.train_dataset:
+            args.train_dataset = str(d_path / Path("train_manifest.json"))
+        if not args.eval_datasets:
+            args.eval_datasets = [str(d_path / Path("test_manifest.json"))]
+
+    data_loader_layer = nemo_asr.AudioToTextDataLayer
+
+    if args.remote_data:
+        train_dl_params["rpyc_host"] = args.remote_data
+        data_loader_layer = RpycAudioToTextDataLayer
+
+    # data_layer = data_loader_layer(
+    #     manifest_filepath=args.train_dataset,
+    #     sample_rate=sample_rate,
+    #     labels=vocab,
+    #     batch_size=args.batch_size,
+    #     num_workers=cpu_per_traindl,
+    #     **train_dl_params,
+    #     # normalize_transcripts=False
+    # )
+    #
+    # N = len(data_layer)
+    # steps_per_epoch = math.ceil(
+    #     N / (args.batch_size * args.iter_per_step * args.num_gpus)
+    # )
+    # logging.info("Have {0} examples to train on.".format(N))
+    #
+    data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor(
+        sample_rate=sample_rate, **jasper_params["AudioToMelSpectrogramPreprocessor"]
+    )
+
+    # multiply_batch_config = jasper_params.get("MultiplyBatch", None)
+    # if multiply_batch_config:
+    #     multiply_batch = nemo_asr.MultiplyBatch(**multiply_batch_config)
+    #
+    # spectr_augment_config = jasper_params.get("SpectrogramAugmentation", None)
+    # if spectr_augment_config:
+    #     data_spectr_augmentation = nemo_asr.SpectrogramAugmentation(
+    #         **spectr_augment_config
+    #     )
+    #
+    eval_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"])
+    eval_dl_params.update(jasper_params["AudioToTextDataLayer"]["eval"])
+    if args.remote_data:
+        eval_dl_params["rpyc_host"] = args.remote_data
+    del eval_dl_params["train"]
+    del eval_dl_params["eval"]
+    data_layers_eval = []
+
+    # if args.eval_datasets:
+    for eval_datasets in args.eval_datasets:
+        data_layer_eval = data_loader_layer(
+            manifest_filepath=eval_datasets,
+            sample_rate=sample_rate,
+            labels=vocab,
+            batch_size=args.eval_batch_size,
+            num_workers=cpu_per_traindl,
+            **eval_dl_params,
+        )
+
+        data_layers_eval.append(data_layer_eval)
+    # else:
+    #     logging.warning("There were no val datasets passed")
+
+    jasper_encoder = nemo_asr.JasperEncoder(
+        feat_in=jasper_params["AudioToMelSpectrogramPreprocessor"]["features"],
+        **jasper_params["JasperEncoder"],
+    )
+    jasper_encoder.restore_from(args.encoder_checkpoint, local_rank=0)
+
+    jasper_decoder = nemo_asr.JasperDecoderForCTC(
+        feat_in=jasper_params["JasperEncoder"]["jasper"][-1]["filters"],
+        num_classes=len(vocab),
+    )
+    jasper_decoder.restore_from(args.decoder_checkpoint, local_rank=0)
+
+    ctc_loss = nemo_asr.CTCLossNM(num_classes=len(vocab))
+
+    greedy_decoder = nemo_asr.GreedyCTCDecoder()
+
+    # logging.info("================================")
+    # logging.info(f"Number of parameters in encoder: {jasper_encoder.num_weights}")
+    # logging.info(f"Number of parameters in decoder: {jasper_decoder.num_weights}")
+    # logging.info(
+    #     f"Total number of parameters in model: "
+    #     f"{jasper_decoder.num_weights + jasper_encoder.num_weights}"
+    # )
+    # logging.info("================================")
+    #
+    # # Train DAG
+    # (audio_signal_t, a_sig_length_t, transcript_t, transcript_len_t) = data_layer()
+    # processed_signal_t, p_length_t = data_preprocessor(
+    #     input_signal=audio_signal_t, length=a_sig_length_t
+    # )
+    #
+    # if multiply_batch_config:
+    #     (
+    #         processed_signal_t,
+    #         p_length_t,
+    #         transcript_t,
+    #         transcript_len_t,
+    #     ) = multiply_batch(
+    #         in_x=processed_signal_t,
+    #         in_x_len=p_length_t,
+    #         in_y=transcript_t,
+    #         in_y_len=transcript_len_t,
+    #     )
+    #
+    # if spectr_augment_config:
+    #     processed_signal_t = data_spectr_augmentation(input_spec=processed_signal_t)
+    #
+    # encoded_t, encoded_len_t = jasper_encoder(
+    #     audio_signal=processed_signal_t, length=p_length_t
+    # )
+    # log_probs_t = jasper_decoder(encoder_output=encoded_t)
+    # predictions_t = greedy_decoder(log_probs=log_probs_t)
+    # loss_t = ctc_loss(
+    #     log_probs=log_probs_t,
+    #     targets=transcript_t,
+    #     input_length=encoded_len_t,
+    #     target_length=transcript_len_t,
+    # )
+    #
+    # # Callbacks needed to print info to console and Tensorboard
+    # train_callback = nemo.core.SimpleLossLoggerCallback(
+    #     tensors=[loss_t, predictions_t, transcript_t, transcript_len_t],
+    #     print_func=partial(monitor_asr_train_progress, labels=vocab),
+    #     get_tb_values=lambda x: [("loss", x[0])],
+    #     tb_writer=neural_factory.tb_writer,
+    # )
+    #
+    # chpt_callback = nemo.core.CheckpointCallback(
+    #     folder=neural_factory.checkpoint_dir,
+    #     load_from_folder=args.load_dir,
+    #     step_freq=args.checkpoint_save_freq,
+    #     checkpoints_to_keep=30,
+    # )
+    #
+    # callbacks = [train_callback, chpt_callback]
+    callbacks = []
+    # assemble eval DAGs
+    for i, eval_dl in enumerate(data_layers_eval):
+        (audio_signal_e, a_sig_length_e, transcript_e, transcript_len_e) = eval_dl()
+        processed_signal_e, p_length_e = data_preprocessor(
+            input_signal=audio_signal_e, length=a_sig_length_e
+        )
+        encoded_e, encoded_len_e = jasper_encoder(
+            audio_signal=processed_signal_e, length=p_length_e
+        )
+        log_probs_e = jasper_decoder(encoder_output=encoded_e)
+        predictions_e = greedy_decoder(log_probs=log_probs_e)
+        loss_e = ctc_loss(
+            log_probs=log_probs_e,
+            targets=transcript_e,
+            input_length=encoded_len_e,
+            target_length=transcript_len_e,
+        )
+
+        # create corresponding eval callback
+        tagname = os.path.basename(args.eval_datasets[i]).split(".")[0]
+        eval_callback = nemo.core.EvaluatorCallback(
+            eval_tensors=[loss_e, predictions_e, transcript_e, transcript_len_e],
+            user_iter_callback=partial(process_evaluation_batch, labels=vocab),
+            user_epochs_done_callback=partial(process_evaluation_epoch, tag=tagname),
+            eval_step=args.eval_freq,
+            tb_writer=neural_factory.tb_writer,
+        )
+
+        callbacks.append(eval_callback)
+    return callbacks
+
+
+def main():
+    args = parse_args()
+    # name = construct_name(
+    #     args.exp_name,
+    #     args.lr,
+    #     args.batch_size,
+    #     args.max_steps,
+    #     args.num_epochs,
+    #     args.weight_decay,
+    #     args.optimizer,
+    #     args.iter_per_step,
+    # )
+    # log_dir = name
+    # if args.work_dir:
+    #     log_dir = os.path.join(args.work_dir, name)
+
+    # instantiate Neural Factory with supported backend
+    neural_factory = nemo.core.NeuralModuleFactory(
+        placement=nemo.core.DeviceType.GPU,
+        backend=nemo.core.Backend.PyTorch,
+        # local_rank=args.local_rank,
+        # optimization_level=args.amp_opt_level,
+        # log_dir=log_dir,
+        # checkpoint_dir=args.checkpoint_dir,
+        # create_tb_writer=args.create_tb_writer,
+        # files_to_copy=[args.model_config, __file__],
+        # cudnn_benchmark=args.cudnn_benchmark,
+        # tensorboard_dir=args.tensorboard_dir,
+    )
+    args.num_gpus = neural_factory.world_size
+
+    # checkpoint_dir = neural_factory.checkpoint_dir
+    if args.local_rank is not None:
+        logging.info("Doing ALL GPU")
+
+    # build dags
+    callbacks = create_all_dags(args, neural_factory)
+    # evaluate model
+    neural_factory.eval(callbacks=callbacks)
+
+
+if __name__ == "__main__":
+    main()

jasper/training/__init__.py

@@ -0,0 +1 @@
+

jasper/training/cli.py

@@ -0,0 +1,366 @@
+# Copyright (c) 2019 NVIDIA Corporation
+import argparse
+import copy
+import math
+import os
+from pathlib import Path
+from functools import partial
+
+from ruamel.yaml import YAML
+
+import nemo
+import nemo.collections.asr as nemo_asr
+import nemo.utils.argparse as nm_argparse
+from nemo.collections.asr.helpers import (
+    monitor_asr_train_progress,
+    process_evaluation_batch,
+    process_evaluation_epoch,
+)
+
+from nemo.utils.lr_policies import CosineAnnealing
+from .data_loaders import RpycAudioToTextDataLayer
+
+logging = nemo.logging
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        parents=[nm_argparse.NemoArgParser()],
+        description="Jasper",
+        conflict_handler="resolve",
+    )
+    parser.set_defaults(
+        checkpoint_dir=None,
+        optimizer="novograd",
+        batch_size=64,
+        eval_batch_size=64,
+        lr=0.002,
+        amp_opt_level="O1",
+        create_tb_writer=True,
+        model_config="./train/jasper10x5dr.yaml",
+        work_dir="./train/work",
+        num_epochs=300,
+        weight_decay=0.005,
+        checkpoint_save_freq=100,
+        eval_freq=100,
+        load_dir="./train/models/jasper/",
+        warmup_steps=3,
+        exp_name="jasper-speller",
+    )
+
+    # Overwrite default args
+    parser.add_argument(
+        "--max_steps",
+        type=int,
+        default=None,
+        required=False,
+        help="max number of steps to train",
+    )
+    parser.add_argument(
+        "--num_epochs",
+        type=int,
+        required=False,
+        help="number of epochs to train",
+    )
+    parser.add_argument(
+        "--model_config",
+        type=str,
+        required=False,
+        help="model configuration file: model.yaml",
+    )
+    parser.add_argument(
+        "--remote_data",
+        type=str,
+        required=False,
+        default="",
+        help="remote dataloader endpoint",
+    )
+    parser.add_argument(
+        "--dataset",
+        type=str,
+        required=False,
+        default="",
+        help="dataset directory containing train/test manifests",
+    )
+
+    # Create new args
+    parser.add_argument("--exp_name", default="Jasper", type=str)
+    parser.add_argument("--beta1", default=0.95, type=float)
+    parser.add_argument("--beta2", default=0.25, type=float)
+    parser.add_argument("--warmup_steps", default=0, type=int)
+    parser.add_argument(
+        "--load_dir",
+        default=None,
+        type=str,
+        help="directory with pre-trained checkpoint",
+    )
+
+    args = parser.parse_args()
+    if args.max_steps is None and args.num_epochs is None:
+        raise ValueError("Either max_steps or num_epochs should be provided.")
+    return args
+
+
+def construct_name(
+    name, lr, batch_size, max_steps, num_epochs, wd, optimizer, iter_per_step
+):
+    if max_steps is not None:
+        return "{0}-lr_{1}-bs_{2}-s_{3}-wd_{4}-opt_{5}-ips_{6}".format(
+            name, lr, batch_size, max_steps, wd, optimizer, iter_per_step
+        )
+    else:
+        return "{0}-lr_{1}-bs_{2}-e_{3}-wd_{4}-opt_{5}-ips_{6}".format(
+            name, lr, batch_size, num_epochs, wd, optimizer, iter_per_step
+        )
+
+
+def create_all_dags(args, neural_factory):
+    yaml = YAML(typ="safe")
+    with open(args.model_config) as f:
+        jasper_params = yaml.load(f)
+    vocab = jasper_params["labels"]
+    sample_rate = jasper_params["sample_rate"]
+
+    # Calculate num_workers for dataloader
+    total_cpus = os.cpu_count()
+    cpu_per_traindl = max(int(total_cpus / neural_factory.world_size), 1)
+    # perturb_config = jasper_params.get('perturb', None)
+    train_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"])
+    train_dl_params.update(jasper_params["AudioToTextDataLayer"]["train"])
+    del train_dl_params["train"]
+    del train_dl_params["eval"]
+    # del train_dl_params["normalize_transcripts"]
+
+    if args.dataset:
+        d_path = Path(args.dataset)
+        if not args.train_dataset:
+            args.train_dataset = str(d_path / Path("train_manifest.json"))
+        if not args.eval_datasets:
+            args.eval_datasets = [str(d_path / Path("test_manifest.json"))]
+
+    data_loader_layer = nemo_asr.AudioToTextDataLayer
+
+    if args.remote_data:
+        train_dl_params["rpyc_host"] = args.remote_data
+        data_loader_layer = RpycAudioToTextDataLayer
+
+    data_layer = data_loader_layer(
+        manifest_filepath=args.train_dataset,
+        sample_rate=sample_rate,
+        labels=vocab,
+        batch_size=args.batch_size,
+        num_workers=cpu_per_traindl,
+        **train_dl_params,
+        # normalize_transcripts=False
+    )
+
+    N = len(data_layer)
+    steps_per_epoch = math.ceil(
+        N / (args.batch_size * args.iter_per_step * args.num_gpus)
+    )
+    logging.info("Have {0} examples to train on.".format(N))
+
+    data_preprocessor = nemo_asr.AudioToMelSpectrogramPreprocessor(
+        sample_rate=sample_rate, **jasper_params["AudioToMelSpectrogramPreprocessor"]
+    )
+
+    multiply_batch_config = jasper_params.get("MultiplyBatch", None)
+    if multiply_batch_config:
+        multiply_batch = nemo_asr.MultiplyBatch(**multiply_batch_config)
+
+    spectr_augment_config = jasper_params.get("SpectrogramAugmentation", None)
+    if spectr_augment_config:
+        data_spectr_augmentation = nemo_asr.SpectrogramAugmentation(
+            **spectr_augment_config
+        )
+
+    eval_dl_params = copy.deepcopy(jasper_params["AudioToTextDataLayer"])
+    eval_dl_params.update(jasper_params["AudioToTextDataLayer"]["eval"])
+    if args.remote_data:
+        eval_dl_params["rpyc_host"] = args.remote_data
+    del eval_dl_params["train"]
+    del eval_dl_params["eval"]
+    data_layers_eval = []
+
+    if args.eval_datasets:
+        for eval_datasets in args.eval_datasets:
+            data_layer_eval = data_loader_layer(
+                manifest_filepath=eval_datasets,
+                sample_rate=sample_rate,
+                labels=vocab,
+                batch_size=args.eval_batch_size,
+                num_workers=cpu_per_traindl,
+                **eval_dl_params,
+            )
+
+            data_layers_eval.append(data_layer_eval)
+    else:
+        logging.warning("There were no val datasets passed")
+
+    jasper_encoder = nemo_asr.JasperEncoder(
+        feat_in=jasper_params["AudioToMelSpectrogramPreprocessor"]["features"],
+        **jasper_params["JasperEncoder"],
+    )
+
+    jasper_decoder = nemo_asr.JasperDecoderForCTC(
+        feat_in=jasper_params["JasperEncoder"]["jasper"][-1]["filters"],
+        num_classes=len(vocab),
+    )
+
+    ctc_loss = nemo_asr.CTCLossNM(num_classes=len(vocab))
+
+    greedy_decoder = nemo_asr.GreedyCTCDecoder()
+
+    logging.info("================================")
+    logging.info(f"Number of parameters in encoder: {jasper_encoder.num_weights}")
+    logging.info(f"Number of parameters in decoder: {jasper_decoder.num_weights}")
+    logging.info(
+        f"Total number of parameters in model: "
+        f"{jasper_decoder.num_weights + jasper_encoder.num_weights}"
+    )
+    logging.info("================================")
+
+    # Train DAG
+    (audio_signal_t, a_sig_length_t, transcript_t, transcript_len_t) = data_layer()
+    processed_signal_t, p_length_t = data_preprocessor(
+        input_signal=audio_signal_t, length=a_sig_length_t
+    )
+
+    if multiply_batch_config:
+        (
+            processed_signal_t,
+            p_length_t,
+            transcript_t,
+            transcript_len_t,
+        ) = multiply_batch(
+            in_x=processed_signal_t,
+            in_x_len=p_length_t,
+            in_y=transcript_t,
+            in_y_len=transcript_len_t,
+        )
+
+    if spectr_augment_config:
+        processed_signal_t = data_spectr_augmentation(input_spec=processed_signal_t)
+
+    encoded_t, encoded_len_t = jasper_encoder(
+        audio_signal=processed_signal_t, length=p_length_t
+    )
+    log_probs_t = jasper_decoder(encoder_output=encoded_t)
+    predictions_t = greedy_decoder(log_probs=log_probs_t)
+    loss_t = ctc_loss(
+        log_probs=log_probs_t,
+        targets=transcript_t,
+        input_length=encoded_len_t,
+        target_length=transcript_len_t,
+    )
+
+    # Callbacks needed to print info to console and Tensorboard
+    train_callback = nemo.core.SimpleLossLoggerCallback(
+        tensors=[loss_t, predictions_t, transcript_t, transcript_len_t],
+        print_func=partial(monitor_asr_train_progress, labels=vocab),
+        get_tb_values=lambda x: [("loss", x[0])],
+        tb_writer=neural_factory.tb_writer,
+    )
+
+    chpt_callback = nemo.core.CheckpointCallback(
+        folder=neural_factory.checkpoint_dir,
+        load_from_folder=args.load_dir,
+        step_freq=args.checkpoint_save_freq,
+        checkpoints_to_keep=30,
+    )
+
+    callbacks = [train_callback, chpt_callback]
+
+    # assemble eval DAGs
+    for i, eval_dl in enumerate(data_layers_eval):
+        (audio_signal_e, a_sig_length_e, transcript_e, transcript_len_e) = eval_dl()
+        processed_signal_e, p_length_e = data_preprocessor(
+            input_signal=audio_signal_e, length=a_sig_length_e
+        )
+        encoded_e, encoded_len_e = jasper_encoder(
+            audio_signal=processed_signal_e, length=p_length_e
+        )
+        log_probs_e = jasper_decoder(encoder_output=encoded_e)
+        predictions_e = greedy_decoder(log_probs=log_probs_e)
+        loss_e = ctc_loss(
+            log_probs=log_probs_e,
+            targets=transcript_e,
+            input_length=encoded_len_e,
+            target_length=transcript_len_e,
+        )
+
+        # create corresponding eval callback
+        tagname = os.path.basename(args.eval_datasets[i]).split(".")[0]
+        eval_callback = nemo.core.EvaluatorCallback(
+            eval_tensors=[loss_e, predictions_e, transcript_e, transcript_len_e],
+            user_iter_callback=partial(process_evaluation_batch, labels=vocab),
+            user_epochs_done_callback=partial(process_evaluation_epoch, tag=tagname),
+            eval_step=args.eval_freq,
+            tb_writer=neural_factory.tb_writer,
+        )
+
+        callbacks.append(eval_callback)
+    return loss_t, callbacks, steps_per_epoch
+
+
+def main():
+    args = parse_args()
+    name = construct_name(
+        args.exp_name,
+        args.lr,
+        args.batch_size,
+        args.max_steps,
+        args.num_epochs,
+        args.weight_decay,
+        args.optimizer,
+        args.iter_per_step,
+    )
+    log_dir = name
+    if args.work_dir:
+        log_dir = os.path.join(args.work_dir, name)
+
+    # instantiate Neural Factory with supported backend
+    neural_factory = nemo.core.NeuralModuleFactory(
+        backend=nemo.core.Backend.PyTorch,
+        local_rank=args.local_rank,
+        optimization_level=args.amp_opt_level,
+        log_dir=log_dir,
+        checkpoint_dir=args.checkpoint_dir,
+        create_tb_writer=args.create_tb_writer,
+        files_to_copy=[args.model_config, __file__],
+        cudnn_benchmark=args.cudnn_benchmark,
+        tensorboard_dir=args.tensorboard_dir,
+    )
+    args.num_gpus = neural_factory.world_size
+
+    checkpoint_dir = neural_factory.checkpoint_dir
+    if args.local_rank is not None:
+        logging.info("Doing ALL GPU")
+
+    # build dags
+    train_loss, callbacks, steps_per_epoch = create_all_dags(args, neural_factory)
+    # train model
+    neural_factory.train(
+        tensors_to_optimize=[train_loss],
+        callbacks=callbacks,
+        lr_policy=CosineAnnealing(
+            args.max_steps
+            if args.max_steps is not None
+            else args.num_epochs * steps_per_epoch,
+            warmup_steps=args.warmup_steps,
+        ),
+        optimizer=args.optimizer,
+        optimization_params={
+            "num_epochs": args.num_epochs,
+            "max_steps": args.max_steps,
+            "lr": args.lr,
+            "betas": (args.beta1, args.beta2),
+            "weight_decay": args.weight_decay,
+            "grad_norm_clip": None,
+        },
+        batches_per_step=args.iter_per_step,
+    )
+
+
+if __name__ == "__main__":
+    main()

jasper/training/data_loaders.py

@@ -0,0 +1,334 @@
+from functools import partial
+import tempfile
+
+# from typing import Any, Dict, List, Optional
+
+import torch
+import nemo
+
+# import nemo.collections.asr as nemo_asr
+from nemo.backends.pytorch import DataLayerNM
+from nemo.core import DeviceType
+
+# from nemo.core.neural_types import *
+from nemo.core.neural_types import NeuralType, AudioSignal, LengthsType, LabelsType
+from nemo.utils.decorators import add_port_docs
+
+from nemo.collections.asr.parts.dataset import (
+    # AudioDataset,
+    # AudioLabelDataset,
+    # KaldiFeatureDataset,
+    # TranscriptDataset,
+    parsers,
+    collections,
+    seq_collate_fn,
+)
+
+# from functools import lru_cache
+import rpyc
+from concurrent.futures import ThreadPoolExecutor
+from tqdm import tqdm
+from .featurizer import RpycWaveformFeaturizer
+
+# from nemo.collections.asr.parts.features import WaveformFeaturizer
+
+# from nemo.collections.asr.parts.perturb import AudioAugmentor, perturbation_types
+
+
+logging = nemo.logging
+
+
+class CachedAudioDataset(torch.utils.data.Dataset):
+    """
+    Dataset that loads tensors via a json file containing paths to audio
+    files, transcripts, and durations (in seconds). Each new line is a
+    different sample. Example below:
+
+    {"audio_filepath": "/path/to/audio.wav", "text_filepath":
+    "/path/to/audio.txt", "duration": 23.147}
+    ...
+    {"audio_filepath": "/path/to/audio.wav", "text": "the
+    transcription", offset": 301.75, "duration": 0.82, "utt":
+    "utterance_id", "ctm_utt": "en_4156", "side": "A"}
+
+    Args:
+        manifest_filepath: Path to manifest json as described above. Can
+            be comma-separated paths.
+        labels: String containing all the possible characters to map to
+        featurizer: Initialized featurizer class that converts paths of
+            audio to feature tensors
+        max_duration: If audio exceeds this length, do not include in dataset
+        min_duration: If audio is less than this length, do not include
+            in dataset
+        max_utts: Limit number of utterances
+        blank_index: blank character index, default = -1
+        unk_index: unk_character index, default = -1
+        normalize: whether to normalize transcript text (default): True
+        bos_id: Id of beginning of sequence symbol to append if not None
+        eos_id: Id of end of sequence symbol to append if not None
+        load_audio: Boolean flag indicate whether do or not load audio
+    """
+
+    def __init__(
+        self,
+        manifest_filepath,
+        labels,
+        featurizer,
+        max_duration=None,
+        min_duration=None,
+        max_utts=0,
+        blank_index=-1,
+        unk_index=-1,
+        normalize=True,
+        trim=False,
+        bos_id=None,
+        eos_id=None,
+        load_audio=True,
+        parser="en",
+    ):
+        self.collection = collections.ASRAudioText(
+            manifests_files=manifest_filepath.split(","),
+            parser=parsers.make_parser(
+                labels=labels,
+                name=parser,
+                unk_id=unk_index,
+                blank_id=blank_index,
+                do_normalize=normalize,
+            ),
+            min_duration=min_duration,
+            max_duration=max_duration,
+            max_number=max_utts,
+        )
+        self.index_feature_map = {}
+
+        self.featurizer = featurizer
+        self.trim = trim
+        self.eos_id = eos_id
+        self.bos_id = bos_id
+        self.load_audio = load_audio
+        print(f"initializing dataset {manifest_filepath}")
+
+        def exec_func(i):
+            return self[i]
+
+        task_count = len(self.collection)
+        with ThreadPoolExecutor() as exe:
+            print("starting all loading tasks")
+            list(
+                tqdm(
+                    exe.map(exec_func, range(task_count)),
+                    position=0,
+                    leave=True,
+                    total=task_count,
+                )
+            )
+        print(f"initializing complete")
+
+    def __getitem__(self, index):
+        sample = self.collection[index]
+        if self.load_audio:
+            cached_features = self.index_feature_map.get(index)
+            if cached_features is not None:
+                features = cached_features
+            else:
+                features = self.featurizer.process(
+                    sample.audio_file,
+                    offset=0,
+                    duration=sample.duration,
+                    trim=self.trim,
+                )
+                self.index_feature_map[index] = features
+            f, fl = features, torch.tensor(features.shape[0]).long()
+        else:
+            f, fl = None, None
+
+        t, tl = sample.text_tokens, len(sample.text_tokens)
+        if self.bos_id is not None:
+            t = [self.bos_id] + t
+            tl += 1
+        if self.eos_id is not None:
+            t = t + [self.eos_id]
+            tl += 1
+
+        return f, fl, torch.tensor(t).long(), torch.tensor(tl).long()
+
+    def __len__(self):
+        return len(self.collection)
+
+
+class RpycAudioToTextDataLayer(DataLayerNM):
+    """Data Layer for general ASR tasks.
+
+    Module which reads ASR labeled data. It accepts comma-separated
+    JSON manifest files describing the correspondence between wav audio files
+    and their transcripts. JSON files should be of the following format::
+
+        {"audio_filepath": path_to_wav_0, "duration": time_in_sec_0, "text": \
+transcript_0}
+        ...
+        {"audio_filepath": path_to_wav_n, "duration": time_in_sec_n, "text": \
+transcript_n}
+
+    Args:
+        manifest_filepath (str): Dataset parameter.
+            Path to JSON containing data.
+        labels (list): Dataset parameter.
+            List of characters that can be output by the ASR model.
+            For Jasper, this is the 28 character set {a-z '}. The CTC blank
+            symbol is automatically added later for models using ctc.
+        batch_size (int): batch size
+        sample_rate (int): Target sampling rate for data. Audio files will be
+            resampled to sample_rate if it is not already.
+            Defaults to 16000.
+        int_values (bool): Bool indicating whether the audio file is saved as
+            int data or float data.
+            Defaults to False.
+        eos_id (id): Dataset parameter.
+            End of string symbol id used for seq2seq models.
+            Defaults to None.
+        min_duration (float): Dataset parameter.
+            All training files which have a duration less than min_duration
+            are dropped. Note: Duration is read from the manifest JSON.
+            Defaults to 0.1.
+        max_duration (float): Dataset parameter.
+            All training files which have a duration more than max_duration
+            are dropped. Note: Duration is read from the manifest JSON.
+            Defaults to None.
+        normalize_transcripts (bool): Dataset parameter.
+            Whether to use automatic text cleaning.
+            It is highly recommended to manually clean text for best results.
+            Defaults to True.
+        trim_silence (bool): Whether to use trim silence from beginning and end
+            of audio signal using librosa.effects.trim().
+            Defaults to False.
+        load_audio (bool): Dataset parameter.
+            Controls whether the dataloader loads the audio signal and
+            transcript or just the transcript.
+            Defaults to True.
+        drop_last (bool): See PyTorch DataLoader.
+            Defaults to False.
+        shuffle (bool): See PyTorch DataLoader.
+            Defaults to True.
+        num_workers (int): See PyTorch DataLoader.
+            Defaults to 0.
+        perturb_config (dict): Currently disabled.
+    """
+
+    @property
+    @add_port_docs()
+    def output_ports(self):
+        """Returns definitions of module output ports.
+        """
+        return {
+            # 'audio_signal': NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}),
+            # 'a_sig_length': NeuralType({0: AxisType(BatchTag)}),
+            # 'transcripts': NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}),
+            # 'transcript_length': NeuralType({0: AxisType(BatchTag)}),
+            "audio_signal": NeuralType(
+                ("B", "T"),
+                AudioSignal(freq=self._sample_rate)
+                if self is not None and self._sample_rate is not None
+                else AudioSignal(),
+            ),
+            "a_sig_length": NeuralType(tuple("B"), LengthsType()),
+            "transcripts": NeuralType(("B", "T"), LabelsType()),
+            "transcript_length": NeuralType(tuple("B"), LengthsType()),
+        }
+
+    def __init__(
+        self,
+        manifest_filepath,
+        labels,
+        batch_size,
+        sample_rate=16000,
+        int_values=False,
+        bos_id=None,
+        eos_id=None,
+        pad_id=None,
+        min_duration=0.1,
+        max_duration=None,
+        normalize_transcripts=True,
+        trim_silence=False,
+        load_audio=True,
+        rpyc_host="",
+        drop_last=False,
+        shuffle=True,
+        num_workers=0,
+    ):
+        super().__init__()
+        self._sample_rate = sample_rate
+
+        def rpyc_root_fn():
+            return rpyc.connect(
+                rpyc_host, 8064, config={"sync_request_timeout": 600}
+            ).root
+
+        rpyc_conn = rpyc_root_fn()
+
+        self._featurizer = RpycWaveformFeaturizer(
+            sample_rate=self._sample_rate,
+            int_values=int_values,
+            augmentor=None,
+            rpyc_conn=rpyc_conn,
+        )
+
+        def read_remote_manifests():
+            local_mp = []
+            for mrp in manifest_filepath.split(","):
+                md = rpyc_conn.read_path(mrp)
+                mf = tempfile.NamedTemporaryFile(
+                    dir="/tmp", prefix="jasper_manifest.", delete=False
+                )
+                mf.write(md)
+                mf.close()
+                local_mp.append(mf.name)
+            return ",".join(local_mp)
+
+        local_manifest_filepath = read_remote_manifests()
+        dataset_params = {
+            "manifest_filepath": local_manifest_filepath,
+            "labels": labels,
+            "featurizer": self._featurizer,
+            "max_duration": max_duration,
+            "min_duration": min_duration,
+            "normalize": normalize_transcripts,
+            "trim": trim_silence,
+            "bos_id": bos_id,
+            "eos_id": eos_id,
+            "load_audio": load_audio,
+        }
+
+        self._dataset = CachedAudioDataset(**dataset_params)
+        self._batch_size = batch_size
+
+        # Set up data loader
+        if self._placement == DeviceType.AllGpu:
+            logging.info("Parallelizing Datalayer.")
+            sampler = torch.utils.data.distributed.DistributedSampler(self._dataset)
+        else:
+            sampler = None
+
+        if batch_size == -1:
+            batch_size = len(self._dataset)
+
+        pad_id = 0 if pad_id is None else pad_id
+        self._dataloader = torch.utils.data.DataLoader(
+            dataset=self._dataset,
+            batch_size=batch_size,
+            collate_fn=partial(seq_collate_fn, token_pad_value=pad_id),
+            drop_last=drop_last,
+            shuffle=shuffle if sampler is None else False,
+            sampler=sampler,
+            num_workers=1,
+        )
+
+    def __len__(self):
+        return len(self._dataset)
+
+    @property
+    def dataset(self):
+        return None
+
+    @property
+    def data_iterator(self):
+        return self._dataloader

jasper/training/featurizer.py

@@ -0,0 +1,51 @@
+# import math
+
+# import librosa
+import torch
+import pickle
+# import torch.nn as nn
+# from torch_stft import STFT
+
+# from nemo import logging
+from nemo.collections.asr.parts.perturb import AudioAugmentor
+# from nemo.collections.asr.parts.segment import AudioSegment
+
+
+class RpycWaveformFeaturizer(object):
+    def __init__(
+        self, sample_rate=16000, int_values=False, augmentor=None, rpyc_conn=None
+    ):
+        self.augmentor = augmentor if augmentor is not None else AudioAugmentor()
+        self.sample_rate = sample_rate
+        self.int_values = int_values
+        self.remote_path_samples = rpyc_conn.get_path_samples
+
+    def max_augmentation_length(self, length):
+        return self.augmentor.max_augmentation_length(length)
+
+    def process(self, file_path, offset=0, duration=0, trim=False):
+        audio = self.remote_path_samples(
+            file_path,
+            target_sr=self.sample_rate,
+            int_values=self.int_values,
+            offset=offset,
+            duration=duration,
+            trim=trim,
+        )
+        return torch.tensor(pickle.loads(audio), dtype=torch.float)
+
+    def process_segment(self, audio_segment):
+        self.augmentor.perturb(audio_segment)
+        return torch.tensor(audio_segment, dtype=torch.float)
+
+    @classmethod
+    def from_config(cls, input_config, perturbation_configs=None):
+        if perturbation_configs is not None:
+            aa = AudioAugmentor.from_config(perturbation_configs)
+        else:
+            aa = None
+
+        sample_rate = input_config.get("sample_rate", 16000)
+        int_values = input_config.get("int_values", False)
+
+        return cls(sample_rate=sample_rate, int_values=int_values, augmentor=aa)

setup.py

@@ -1,11 +1,52 @@
-from setuptools import setup
+from setuptools import setup, find_packages
 
 requirements = [
     "ruamel.yaml",
+    "torch==1.4.0",
+    "torchvision==0.5.0",
     "nemo_toolkit[asr] @ git+https://github.com/NVIDIA/NeMo.git@09e3ba4dfe333f86d6c5c1048e07210924294be9#egg=nemo_toolkit",
 ]
 
-extra_requirements = {"server": ["rpyc==4.1.4"]}
+extra_requirements = {
+    "server": ["rpyc~=4.1.4", "tqdm~=4.39.0"],
+    "data": [
+        "google-cloud-texttospeech~=1.0.1",
+        "tqdm~=4.39.0",
+        "pydub~=0.24.0",
+        "scikit_learn~=0.22.1",
+        "pandas~=1.0.3",
+        "boto3~=1.12.35",
+        "ruamel.yaml==0.16.10",
+        "pymongo==3.10.1",
+        "librosa==0.7.2",
+        "matplotlib==3.2.1",
+        "pandas==1.0.3",
+        "tabulate==0.8.7",
+        "natural==0.2.0",
+        "num2words==0.5.10",
+        "typer[all]==0.1.1",
+        "python-slugify==4.0.0",
+        "lenses @ git+https://github.com/ingolemo/python-lenses.git@b2a2a9aa5b61540992d70b2cf36008d0121e8948#egg=lenses",
+    ],
+    "validation": [
+        "rpyc~=4.1.4",
+        "pymongo==3.10.1",
+        "typer[all]==0.1.1",
+        "tqdm~=4.39.0",
+        "librosa==0.7.2",
+        "matplotlib==3.2.1",
+        "pydub~=0.24.0",
+        "streamlit==0.58.0",
+        "natural==0.2.0",
+        "stringcase==1.2.0",
+        "google-cloud-speech~=1.3.1",
+    ]
+    # "train": [
+    #     "torchaudio==0.5.0",
+    #     "torch-stft==0.1.4",
+    # ]
+}
+packages = find_packages()
 
 setup(
     name="jasper-asr",
@@ -17,11 +58,24 @@ setup(
     license="MIT",
     install_requires=requirements,
     extras_require=extra_requirements,
-    packages=["."],
+    packages=packages,
     entry_points={
         "console_scripts": [
             "jasper_transcribe = jasper.transcribe:main",
-            "jasper_asr_rpyc_server = jasper.server:main",
+            "jasper_server = jasper.server:main",
+            "jasper_trainer = jasper.training.cli:main",
+            "jasper_evaluator = jasper.evaluate:main",
+            "jasper_data_tts_generate = jasper.data.tts_generator:main",
+            "jasper_data_conv_generate = jasper.data.conv_generator:main",
+            "jasper_data_nlu_generate = jasper.data.nlu_generator:main",
+            "jasper_data_test_generate = jasper.data.test_generator:main",
+            "jasper_data_call_recycle = jasper.data.call_recycler:main",
+            "jasper_data_asr_recycle = jasper.data.asr_recycler:main",
+            "jasper_data_rev_recycle = jasper.data.rev_recycler:main",
+            "jasper_data_server = jasper.data.server:main",
+            "jasper_data_validation = jasper.data.validation.process:main",
+            "jasper_data_preprocess = jasper.data.process:main",
+            "jasper_data_slu_evaluate = jasper.data.slu_evaluator:main",
         ]
     },
     zip_safe=False,

validation_ui.py

@@ -0,0 +1,3 @@
+import runpy
+
+runpy.run_module("jasper.data.validation.ui", run_name="__main__", alter_sys=True)