Merge pull request #3 from malarinv/dependabot/pip/torch-2.8.0

Bump torch from 1.4.0 to 2.8.0

.flake8

@@ -0,0 +1,4 @@
+[flake8]
+exclude = docs
+ignore = E203, W503
+max-line-length = 119

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)
+```

README.md

@@ -7,10 +7,16 @@
 
 # Table of Contents
 
+* [Prerequisites](#prerequisites)
 * [Features](#features)
 * [Installation](#installation)
 * [Usage](#usage)
 
+# Prerequisites
+```bash
+# apt install libsndfile-dev ffmpeg
+```
+
 # Features
 
 * ASR using Jasper (from [NemoToolkit](https://github.com/NVIDIA/NeMo) )

jasper/data/asr_recycler.py

@@ -1,104 +0,0 @@
-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

@@ -1,521 +0,0 @@
-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
-    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 get_call_meta(call_obj):
-        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 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_meta(call_obj)
-        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

@@ -1,68 +0,0 @@
-import typer
-from pathlib import Path
-from random import randrange
-from itertools import product
-from math import floor
-
-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()
-
-    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),
-        ]
-
-    day_months = [dm for d, m in product(days, months) for dm in canon_vars(d, m)]
-
-    conv_data["dates"] = day_months
-
-    def dates_data_gen():
-        i = randrange(len(day_months))
-        return day_months[i]
-
-    ExtendedPath(conv_dest).write_json(conv_data)
-
-
-def main():
-    app()
-
-
-if __name__ == "__main__":
-    main()

jasper/data/process.py

@@ -23,7 +23,7 @@ def fixate_data(dataset_path: Path):
 
 
 @app.command()
-def augment_datasets(src_dataset_paths: List[Path], dest_dataset_path: Path):
+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:
@@ -38,9 +38,9 @@ def augment_datasets(src_dataset_paths: List[Path], dest_dataset_path: Path):
 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)
+    train_data, test_data = train_test_split(asr_data, test_size=test_size)
+    asr_manifest_writer(manifest_path.with_name("train_manifest.json"), train_data)
+    asr_manifest_writer(manifest_path.with_name("test_manifest.json"), test_data)
 
 
 @app.command()
@@ -52,9 +52,9 @@ def validate_data(dataset_path: Path):
         data_file = dataset_path / Path(mf_type)
         print(f"validating {data_file}.")
         with Path(data_file).open("r") as pf:
-            pnr_jsonl = pf.readlines()
+            data_jsonl = pf.readlines()
         duration = 0
-        for (i, s) in enumerate(pnr_jsonl):
+        for (i, s) in enumerate(data_jsonl):
             try:
                 d = json.loads(s)
                 duration += d["duration"]

jasper/data/rastrik_recycler.py

@@ -0,0 +1,93 @@
+from rastrik.proto.callrecord_pb2 import CallRecord
+import gzip
+from pydub import AudioSegment
+from .utils import ui_dump_manifest_writer, strip_silence
+
+import typer
+from itertools import chain
+from io import BytesIO
+from pathlib import Path
+
+app = typer.Typer()
+
+
+@app.command()
+def extract_manifest(
+    call_log_dir: Path = Path("./data/call_audio"),
+    output_dir: Path = Path("./data"),
+    dataset_name: str = "grassroot_pizzahut_v1",
+    caller_name: str = "grassroot",
+    verbose: bool = False,
+):
+    call_asr_data: Path = output_dir / Path("asr_data")
+    call_asr_data.mkdir(exist_ok=True, parents=True)
+
+    def wav_pb2_generator(log_dir):
+        for wav_path in log_dir.glob("**/*.wav"):
+            if verbose:
+                typer.echo(f"loading events for file {wav_path}")
+            call_wav = AudioSegment.from_file_using_temporary_files(wav_path)
+            meta_path = wav_path.with_suffix(".pb2.gz")
+            yield call_wav, wav_path, meta_path
+
+    def read_event(call_wav, log_file):
+        call_wav_0, call_wav_1 = call_wav.split_to_mono()
+        with gzip.open(log_file, "rb") as log_h:
+            record_data = log_h.read()
+        cr = CallRecord()
+        cr.ParseFromString(record_data)
+
+        first_audio_event_timestamp = next(
+            (
+                i
+                for i in cr.events
+                if i.WhichOneof("event_type") == "call_event"
+                and i.call_event.WhichOneof("event_type") == "call_audio"
+            )
+        ).timestamp.ToDatetime()
+
+        speech_events = [
+            i
+            for i in cr.events
+            if i.WhichOneof("event_type") == "speech_event"
+            and i.speech_event.WhichOneof("event_type") == "asr_final"
+        ]
+        previous_event_timestamp = (
+            first_audio_event_timestamp - first_audio_event_timestamp
+        )
+        for index, each_speech_events in enumerate(speech_events):
+            asr_final = each_speech_events.speech_event.asr_final
+            speech_timestamp = each_speech_events.timestamp.ToDatetime()
+            actual_timestamp = speech_timestamp - first_audio_event_timestamp
+            start_time = previous_event_timestamp.total_seconds() * 1000
+            end_time = actual_timestamp.total_seconds() * 1000
+            audio_segment = strip_silence(call_wav_1[start_time:end_time])
+
+            code_fb = BytesIO()
+            audio_segment.export(code_fb, format="wav")
+            wav_data = code_fb.getvalue()
+            previous_event_timestamp = actual_timestamp
+            duration = (end_time - start_time) / 1000
+            yield asr_final, duration, wav_data, "grassroot", audio_segment
+
+    def generate_call_asr_data():
+        full_data = []
+        total_duration = 0
+        for wav, wav_path, pb2_path in wav_pb2_generator(call_log_dir):
+            asr_data = read_event(wav, pb2_path)
+            total_duration += wav.duration_seconds
+            full_data.append(asr_data)
+        n_calls = len(full_data)
+        typer.echo(f"loaded {n_calls} calls of duration {total_duration}s")
+        n_dps = ui_dump_manifest_writer(call_asr_data, dataset_name, chain(*full_data))
+        typer.echo(f"written {n_dps} data points")
+
+    generate_call_asr_data()
+
+
+def main():
+    app()
+
+
+if __name__ == "__main__":
+    main()

jasper/data/rev_recycler.py

@@ -1,175 +0,0 @@
-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/tts/googletts.py

@@ -1,52 +0,0 @@
-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

@@ -1,26 +0,0 @@
-"""
-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

@@ -1,62 +0,0 @@
-# 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/unique_nlu.py

@@ -1,92 +0,0 @@
-import pandas as pd
-
-
-def compute_pnr_name_city():
-    data = pd.read_csv("./customer_utterance_processing/customer_provide_answer.csv")
-
-    def unique_pnr_count():
-        pnr_data = data[data["Input.Answer"] == "ZZZZZZ"]
-        unique_pnr_set = {
-            t
-            for n in range(1, 5)
-            for t in pnr_data[f"Answer.utterance-{n}"].tolist()
-            if "ZZZZZZ" in t
-        }
-        return len(unique_pnr_set)
-
-    def unique_name_count():
-        pnr_data = data[data["Input.Answer"] == "John Doe"]
-        unique_pnr_set = {
-            t
-            for n in range(1, 5)
-            for t in pnr_data[f"Answer.utterance-{n}"].tolist()
-            if "John Doe" in t
-        }
-        return len(unique_pnr_set)
-
-    def unique_city_count():
-        pnr_data = data[data["Input.Answer"] == "Heathrow Airport"]
-        unique_pnr_set = {
-            t
-            for n in range(1, 5)
-            for t in pnr_data[f"Answer.utterance-{n}"].tolist()
-            if "Heathrow Airport" in t
-        }
-        return len(unique_pnr_set)
-
-    def unique_entity_count(entity_template_tags):
-        # entity_data = data[data['Input.Prompt'] == entity_template_tag]
-        entity_data = 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 len(unique_entity_set)
-
-    print('PNR', unique_pnr_count())
-    print('Name', unique_name_count())
-    print('City', unique_city_count())
-    print('Payment', unique_entity_count(['KPay', 'ZPay', 'Credit Card']))
-
-
-def compute_date():
-    entity_template_tags = ['27 january', 'December 18']
-    data = pd.read_csv("./customer_utterance_processing/customer_provide_departure.csv")
-    # data.sample(10)
-
-    def unique_entity_count(entity_template_tags):
-        # entity_data = data[data['Input.Prompt'] == entity_template_tag]
-        entity_data = 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 len(unique_entity_set)
-
-    print('Date', unique_entity_count(entity_template_tags))
-
-
-def compute_option():
-    entity_template_tag = 'third'
-    data = pd.read_csv("./customer_utterance_processing/customer_provide_flight_selection.csv")
-
-    def unique_entity_count():
-        entity_data = data[data['Input.Prompt'] == entity_template_tag]
-        unique_entity_set = {
-            t
-            for n in range(1, 5)
-            for t in entity_data[f"Answer.utterance-{n}"].tolist()
-            if entity_template_tag in t
-        }
-        return len(unique_entity_set)
-
-    print('Option', unique_entity_count())
-
-
-compute_pnr_name_city()
-compute_date()
-compute_option()

jasper/data/utils.py

@@ -1,22 +1,20 @@
-import numpy as np
-import wave
 import io
 import os
 import json
+import wave
 from pathlib import Path
+from functools import partial
+from uuid import uuid4
+from concurrent.futures import ThreadPoolExecutor
 
 import pymongo
 from slugify import slugify
-from uuid import uuid4
-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
-from functools import partial
-from concurrent.futures import ThreadPoolExecutor
 
 
 def manifest_str(path, dur, text):
@@ -36,27 +34,8 @@ def wav_bytes(audio_bytes, frame_rate=24000):
     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):
@@ -67,11 +46,11 @@ def asr_data_writer(output_dir, dataset_name, asr_data_source, verbose=False):
     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 = str(uuid4()) + "_" + slugify(transcript, max_length=8)
+            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)
+            rel_data_path = audio_file.relative_to(dataset_dir)
+            manifest = manifest_str(str(rel_data_path), audio_dur, transcript)
             mf.write(manifest)
             if verbose:
                 print(f"writing '{transcript}' of duration {audio_dur}")
@@ -79,102 +58,99 @@ def asr_data_writer(output_dir, dataset_name, asr_data_source, verbose=False):
     return num_datapoints
 
 
-def ui_dump_manifest_writer(output_dir, dataset_name, asr_data_source, verbose=False):
+def ui_data_generator(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")
+
+    def data_fn(
+        transcript,
+        audio_dur,
+        wav_data,
+        caller_name,
+        aud_seg,
+        fname,
+        audio_path,
+        num_datapoints,
+        rel_data_path,
+    ):
+        pretrained_result = transcriber_pretrained(aud_seg.raw_data)
+        pretrained_wer = word_error_rate([transcript], [pretrained_result])
+        png_path = Path(fname).with_suffix(".png")
+        wav_plot_path = dataset_dir / Path("wav_plots") / png_path
+        if not wav_plot_path.exists():
+            plot_seg(wav_plot_path, audio_path)
+        return {
+            "audio_filepath": str(rel_data_path),
+            "duration": round(audio_dur, 1),
+            "text": transcript,
+            "real_idx": num_datapoints,
+            "audio_path": audio_path,
+            "spoken": transcript,
+            "caller": caller_name,
+            "utterance_id": fname,
+            "pretrained_asr": pretrained_result,
+            "pretrained_wer": pretrained_wer,
+            "plot_path": str(wav_plot_path),
+        }
+
     num_datapoints = 0
-    ui_dump = {
-        "use_domain_asr": False,
-        "annotation_only": False,
-        "enable_plots": True,
-        "data": [],
-    }
     data_funcs = []
     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)
-            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,
-                "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
-    with ThreadPoolExecutor() as exe:
-        print("starting all plot/transcription tasks")
-        dump_data = list(
-            tqdm(
-                exe.map(lambda x: x(), data_funcs),
-                position=0,
-                leave=True,
-                total=len(data_funcs),
+    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_data_path = audio_file.relative_to(dataset_dir)
+        data_funcs.append(
+            partial(
+                data_fn,
+                transcript,
+                audio_dur,
+                wav_data,
+                caller_name,
+                aud_seg,
+                fname,
+                audio_path,
+                num_datapoints,
+                rel_data_path,
             )
         )
-    ui_dump["data"] = dump_data
-    ExtendedPath(ui_dump_file).write_json(ui_dump)
+        num_datapoints += 1
+    ui_data = parallel_apply(lambda x: x(), data_funcs)
+    return ui_data, num_datapoints
+
+
+def ui_dump_manifest_writer(output_dir, dataset_name, asr_data_source, verbose=False):
+    dataset_dir = output_dir / Path(dataset_name)
+    dump_data, num_datapoints = ui_data_generator(
+        output_dir, dataset_name, asr_data_source, verbose=verbose
+    )
+
+    asr_manifest = dataset_dir / Path("manifest.json")
+    with asr_manifest.open("w") as mf:
+        print(f"writing manifest to {asr_manifest}")
+        for d in dump_data:
+            rel_data_path = d["audio_filepath"]
+            audio_dur = d["duration"]
+            transcript = d["text"]
+            manifest = manifest_str(str(rel_data_path), audio_dur, transcript)
+            mf.write(manifest)
+
+    ui_dump_file = dataset_dir / Path("ui_dump.json")
+    ExtendedPath(ui_dump_file).write_json({"data": dump_data})
     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:
+        data_jsonl = pf.readlines()
+    data_data = [json.loads(v) for v in data_jsonl]
+    for p in data_data:
         p["audio_path"] = data_manifest_path.parent / Path(p["audio_filepath"])
-        p["chars"] = Path(p["audio_filepath"]).stem
+        p["text"] = p["text"].strip()
         yield p
 
 
@@ -188,6 +164,32 @@ def asr_manifest_writer(asr_manifest_path: Path, manifest_str_source):
             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."""
 
@@ -203,12 +205,6 @@ class ExtendedPath(type(Path())):
             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}/"
@@ -234,10 +230,12 @@ def plot_seg(wav_plot_path, audio_path):
         fig.savefig(wav_plot_f, format="png", dpi=50)
 
 
-def main():
-    for c in random_pnr_generator():
-        print(c)
-
-
-if __name__ == "__main__":
-    main()
+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)
+            )
+        ]

jasper/data/validation/process.py

@@ -1,16 +1,14 @@
 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,
 )
@@ -18,9 +16,7 @@ from ..utils import (
 app = typer.Typer()
 
 
-def preprocess_datapoint(
-    idx, rel_root, sample, use_domain_asr, annotation_only, enable_plots
-):
+def preprocess_datapoint(idx, rel_root, sample):
     from pydub import AudioSegment
     from nemo.collections.asr.metrics import word_error_rate
     from jasper.client import transcribe_gen
@@ -30,37 +26,23 @@ def preprocess_datapoint(
         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)
+        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)
+        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"]])
+        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}')
@@ -68,70 +50,59 @@ def preprocess_datapoint(
 
 @app.command()
 def dump_ui(
-    data_name: str = typer.Option("call_alphanum", show_default=True),
+    data_name: str = typer.Option("dataname", 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
+    from io import BytesIO
+    from pydub import AudioSegment
+    from ..utils import ui_data_generator
 
     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()
+    def asr_data_source_gen():
+        with data_manifest_path.open("r") as pf:
+            data_jsonl = pf.readlines()
+            for v in data_jsonl:
+                sample = json.loads(v)
+                rel_root = data_manifest_path.parent
+                res = dict(sample)
+                audio_path = rel_root / Path(sample["audio_filepath"])
+                audio_segment = (
+                    AudioSegment.from_file_using_temporary_files(audio_path)
+                    .set_channels(1)
+                    .set_sample_width(2)
+                    .set_frame_rate(24000)
+                )
+                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)
+                code_fb = BytesIO()
+                audio_segment.export(code_fb, format="wav")
+                wav_data = code_fb.getvalue()
+                duration = audio_segment.duration_seconds
+                asr_final = res["text"]
+                yield asr_final, duration, wav_data, "caller", audio_segment
 
-        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)
+    dump_data, num_datapoints = ui_data_generator(
+        dataset_dir, data_name, asr_data_source_gen()
+    )
+    ui_dump_file = dataset_dir / Path("ui_dump.json")
+    ExtendedPath(ui_dump_file).write_json({"data": dump_data})
 
 
 @app.command()
 def sample_ui(
-    data_name: str = typer.Option("call_upwork_train_cnd", show_default=True),
+    data_name: str = typer.Option("dataname", 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),
@@ -157,7 +128,7 @@ def sample_ui(
 
 @app.command()
 def task_ui(
-    data_name: str = typer.Option("call_upwork_train_cnd", show_default=True),
+    data_name: str = typer.Option("dataname", 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),
@@ -180,14 +151,18 @@ def task_ui(
 
 @app.command()
 def dump_corrections(
-    data_name: str = typer.Option("call_alphanum", show_default=True),
+    task_uid: str,
+    data_name: str = typer.Option("dataname", 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")
-
-    cursor_obj = col.find({"type": "correction"}, projection={"_id": False})
+    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)
 
@@ -195,7 +170,7 @@ def dump_corrections(
 @app.command()
 def caller_quality(
     task_uid: str,
-    data_name: str = typer.Option("call_upwork_train_cnd", show_default=True),
+    data_name: str = typer.Option("dataname", show_default=True),
     dump_dir: Path = Path("./data/asr_data"),
     dump_fname: Path = Path("ui_dump.json"),
     correction_fname: Path = Path("corrections.json"),
@@ -231,7 +206,7 @@ def caller_quality(
 
 @app.command()
 def fill_unannotated(
-    data_name: str = typer.Option("call_alphanum", show_default=True),
+    data_name: str = typer.Option("dataname", show_default=True),
     dump_dir: Path = Path("./data/valiation_data"),
     dump_file: Path = Path("ui_dump.json"),
     corrections_file: Path = Path("corrections.json"),
@@ -252,97 +227,96 @@ def fill_unannotated(
         )
 
 
-class ExtractionType(str, Enum):
-    date = "dates"
-    city = "cities"
-    name = "names"
-
-
 @app.command()
 def split_extract(
-    data_name: str = typer.Option("call_alphanum", show_default=True),
+    data_name: str = typer.Option("dataname", 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/valiation_data"),
+    dump_dir: Path = Path("./data/asr_data"),
     dump_file: Path = Path("ui_dump.json"),
-    manifest_dir: Path = Path("./data/asr_data"),
     manifest_file: Path = Path("manifest.json"),
-    corrections_file: Path = Path("corrections.json"),
-    conv_data_path: Path = Path("./data/conv_data.json"),
-    extraction_type: ExtractionType = ExtractionType.date,
+    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: str = "all",
 ):
     import shutil
 
-    def get_conv_data(cdp):
-        from itertools import product
+    data_manifest_path = dump_dir / Path(data_name) / manifest_file
+    conv_data = ExtendedPath(conv_data_path).read_json()
 
-        conv_data = json.load(cdp.open())
-        days = [str(i) for i in range(1, 32)]
-        months = conv_data["months"]
-        day_months = {d + " " + m for d, m in product(days, months)}
-        return {
-            "cities": set(conv_data["cities"]),
-            "names": set(conv_data["names"]),
-            "dates": day_months,
-        }
+    def extract_data_of_type(extraction_key):
+        extraction_vals = conv_data[extraction_key]
+        dest_data_name = data_name + "_" + extraction_key.lower()
 
-    dest_data_name = data_name + "_" + extraction_type.value
-    data_manifest_path = manifest_dir / Path(data_name) / manifest_file
-    conv_data = get_conv_data(conv_data_path)
-    extraction_vals = conv_data[extraction_type.value]
+        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
 
-    manifest_gen = asr_manifest_reader(data_manifest_path)
-    dest_data_dir = manifest_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_dir = dump_dir / Path(dest_data_name)
-    dest_ui_dir.mkdir(exist_ok=True, parents=True)
-    dest_ui_path = dest_ui_dir / dump_file
-    dest_correction_path = dest_ui_dir / corrections_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
 
-    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))
 
-    asr_manifest_writer(dest_manifest_path, extract_manifest(manifest_gen))
-
-    ui_data_path = dump_dir / Path(data_name) / dump_file
-    corrections_path = dump_dir / Path(data_name) / corrections_file
-    ui_data = json.load(ui_data_path.open())["data"]
-    file_ui_map = {Path(u["audio_filepath"]).stem: u for u in ui_data}
-    corrections = json.load(corrections_path.open())
-
-    extracted_ui_data = list(filter(lambda u: u["text"] in extraction_vals, ui_data))
-    ExtendedPath(dest_ui_path).write_json(extracted_ui_data)
-
-    extracted_corrections = list(
-        filter(
-            lambda c: c["code"] in file_ui_map
-            and file_ui_map[c["code"]]["text"] in extraction_vals,
-            corrections,
+        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)
         )
-    )
-    ExtendedPath(dest_correction_path).write_json(extracted_corrections)
+        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/valiation_data"),
-    manifest_dir: Path = Path("./data/asr_data"),
+    data_name: str = typer.Option("dataname", show_default=True),
+    dump_dir: Path = Path("./data/asr_data"),
     manifest_file: Path = Path("manifest.json"),
     corrections_file: Path = Path("corrections.json"),
-    # data_manifest_path: Path = Path("./data/asr_data/call_alphanum/manifest.json"),
-    # corrections_path: Path = Path("./data/valiation_data/corrections.json"),
-    skip_incorrect: bool = True,
+    ui_dump_file: Path = Path("ui_dump.json"),
+    skip_incorrect: bool = typer.Option(True, show_default=True),
 ):
-    data_manifest_path = manifest_dir / Path(data_name) / manifest_file
+    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(manifest_data_gen, corrections_path):
-        corrections = json.load(corrections_path.open())
+    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"
         }
@@ -355,36 +329,40 @@ def update_corrections(
         # for d in manifest_data_gen:
         #     if d["chars"] in incorrect_set:
         #         d["audio_path"].unlink()
-        renamed_set = set()
-        for d in manifest_data_gen:
-            if d["chars"] in correct_set:
+        # 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["chars"] in correction_map:
-                correct_text = correction_map[d["chars"]]
+            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:
-                    renamed_set.add(correct_text)
-                    new_name = str(Path(correct_text).with_suffix(".wav"))
-                    d["audio_path"].replace(d["audio_path"].with_name(new_name))
+                    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": alnum_to_asr_tokens(correct_text),
+                        "text": correct_text,
                     }
             else:
+                orig_audio_path = Path(d["audio_path"])
                 # don't delete if another correction points to an old file
-                if d["chars"] not in renamed_set:
-                    d["audio_path"].unlink()
-                else:
-                    print(f'skipping deletion of correction:{d["chars"]}')
+                # 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
@@ -393,8 +371,8 @@ def update_corrections(
     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(manifest_gen, corrections_path)
+    # 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)

jasper/data/validation/ui.py

@@ -42,7 +42,9 @@ if not hasattr(st, "mongo_connected"):
             upsert=True,
         )
 
-    def set_task_fn(mf_path):
+    def set_task_fn(mf_path, task_id):
+        if task_id:
+            st.task_id = task_id
         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}")
@@ -66,26 +68,22 @@ def load_ui_data(validation_ui_data_path: Path):
 
 
 @app.command()
-def main(manifest: Path):
-    st.set_task(manifest)
+def main(manifest: Path, task_id: str = ""):
+    st.set_task(manifest, task_id)
     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)
+        st.title(f"ASR Validation - # {task_uid}")
+    st.markdown(f"{sample_no+1} of {len(asr_data)} : **{sample['text']}**")
     new_sample = st.number_input(
         "Go To Sample:", value=sample_no + 1, min_value=1, max_value=len(asr_data)
     )
@@ -94,19 +92,13 @@ def main(manifest: Path):
     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.sidebar.image(Path(sample["plot_path"]).read_bytes())
     st.audio(Path(sample["audio_path"]).open("rb"))
     # set default to text
     corrected = sample["text"]
@@ -128,16 +120,12 @@ def main(manifest: Path):
         )
         st.update_cursor(sample_no + 1)
     if correction_entry:
-        st.markdown(
-            f'Your Response: **{correction_entry["value"]["status"]}** Correction: **{correction_entry["value"]["correction"]}**'
-        )
+        status = correction_entry["value"]["status"]
+        correction = correction_entry["value"]["correction"]
+        st.markdown(f"Your Response: **{status}** Correction: **{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
-        ]
+        candidates = [i for (i, p) in enumerate(asr_data) if p["text"] == text_sample]
         if len(candidates) > 0:
             st.update_cursor(candidates[0])
     real_idx = st.number_input(

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/cli.py

@@ -41,7 +41,7 @@ def parse_args():
         work_dir="./train/work",
         num_epochs=300,
         weight_decay=0.005,
-        checkpoint_save_freq=200,
+        checkpoint_save_freq=100,
         eval_freq=100,
         load_dir="./train/models/jasper/",
         warmup_steps=3,
@@ -266,6 +266,7 @@ def create_all_dags(args, neural_factory):
         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]

setup.py

@@ -2,7 +2,7 @@ from setuptools import setup, find_packages
 
 requirements = [
     "ruamel.yaml",
-    "torch==1.4.0",
+    "torch==2.8.0",
     "torchvision==0.5.0",
     "nemo_toolkit[asr] @ git+https://github.com/NVIDIA/NeMo.git@09e3ba4dfe333f86d6c5c1048e07210924294be9#egg=nemo_toolkit",
 ]
@@ -19,13 +19,15 @@ extra_requirements = {
         "ruamel.yaml==0.16.10",
         "pymongo==3.10.1",
         "librosa==0.7.2",
+        "numba==0.48",
         "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",
+        "typer[all]==0.3.1",
         "python-slugify==4.0.0",
+        "rpyc~=4.1.4",
         "lenses @ git+https://github.com/ingolemo/python-lenses.git@b2a2a9aa5b61540992d70b2cf36008d0121e8948#egg=lenses",
     ],
     "validation": [
@@ -39,6 +41,7 @@ extra_requirements = {
         "streamlit==0.58.0",
         "natural==0.2.0",
         "stringcase==1.2.0",
+        "google-cloud-speech~=1.3.1",
     ]
     # "train": [
     #     "torchaudio==0.5.0",
@@ -63,14 +66,15 @@ setup(
             "jasper_transcribe = jasper.transcribe: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_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_nlu_generate = jasper.data.nlu_generator:main",
+            "jasper_data_rastrik_recycle = jasper.data.rastrik_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,