implement call audio data recycler for asr

.gitignore

@@ -1,3 +1,7 @@
+data/
+.env*
+*.yaml
+
 
 # Created by https://www.gitignore.io/api/python
 # Edit at https://www.gitignore.io/?templates=python
@@ -108,3 +112,36 @@ dmypy.json
 .pyre/
 
 # End of https://www.gitignore.io/api/python
+
+# Created by https://www.gitignore.io/api/macos
+# Edit at https://www.gitignore.io/?templates=macos
+
+### macOS ###
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+# End of https://www.gitignore.io/api/macos

jasper/data_utils/call_recycler.py

@@ -0,0 +1,333 @@
+# import argparse
+
+# import logging
+import typer
+from pathlib import Path
+
+app = typer.Typer()
+# leader_app = typer.Typer()
+# app.add_typer(leader_app, name="leaderboard")
+# plot_app = typer.Typer()
+# app.add_typer(plot_app, name="plot")
+
+
+@app.command()
+def analyze(
+    leaderboard: bool = False,
+    plot_calls: bool = False,
+    extract_data: bool = False,
+    call_logs_file: Path = Path("./call_logs.yaml"),
+    output_dir: Path = Path("./data"),
+):
+    call_logs_file = Path("./call_logs.yaml")
+    output_dir = Path("./data")
+
+    from urllib.parse import urlsplit
+    from functools import reduce
+    from pymongo import MongoClient
+    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
+
+    # from concurrent.futures import ThreadPoolExecutor
+    from dateutil.relativedelta import relativedelta
+    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 asr_data_writer
+    from pydub import AudioSegment
+
+    matplotlib.rcParams["agg.path.chunksize"] = 10000
+
+    matplotlib.use("agg")
+
+    # logging.basicConfig(
+    #     level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+    # )
+    # logger = logging.getLogger(__name__)
+    yaml = YAML()
+    s3 = boto3.client("s3")
+    mongo_collection = MongoClient("mongodb://localhost:27017/").test.calls
+    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)
+
+    call_logs = yaml.load(call_logs_file.read_text())
+
+    def get_call_meta(call_obj):
+        s3_event_url_p = urlsplit(call_obj["DataURI"])
+        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}")
+            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 process_call(call_obj):
+        call_meta = get_call_meta(call_obj)
+        call_events = call_meta["Events"]
+
+        def is_writer_event(ev):
+            return ev["Author"] == "AUDIO_WRITER"
+
+        writer_events = list(filter(is_writer_event, call_events))
+        s3_wav_url = re.search(r"saved to: (.*)", 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)
+
+        def is_utter_event(ev):
+            return (
+                (ev["Author"] == "CONV" or ev["Author"] == "ASR")
+                and (ev["Type"] != "DEBUG")
+                and ev["Type"] != "ASR_RESULT"
+            )
+
+        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}")
+            s3.download_file(
+                s3_wav_url_p.netloc, s3_wav_url_p.path[1:], str(saved_wav_path)
+            )
+
+        # %config InlineBackend.figure_format = "retina"
+        def chunk_n(evs, n):
+            return [evs[i * n : (i + 1) * n] for i in range((len(evs) + n - 1) // n)]
+
+        def get_data_points(utter_events):
+            data_points = []
+            for evs in chunk_n(utter_events, 3):
+                assert evs[0]["Type"] == "CONV_RESULT"
+                assert evs[1]["Type"] == "STARTED_SPEAKING"
+                assert evs[2]["Type"] == "STOPPED_SPEAKING"
+                start_time = get_ev_fev_timedelta(evs[1]).total_seconds() - 1.5
+                end_time = get_ev_fev_timedelta(evs[2]).total_seconds()
+                code = evs[0]["Msg"]
+                data_points.append(
+                    {"start_time": start_time, "end_time": end_time, "code": code}
+                )
+            return data_points
+
+        def plot_events(y, sr, utter_events, file_path):
+            plt.figure(figsize=(16, 12))
+            librosa.display.waveplot(y=y, sr=sr)
+            # plt.tight_layout()
+            for evs in chunk_n(utter_events, 3):
+                assert evs[0]["Type"] == "CONV_RESULT"
+                assert evs[1]["Type"] == "STARTED_SPEAKING"
+                assert evs[2]["Type"] == "STOPPED_SPEAKING"
+                for ev in evs:
+                    # print(ev["Type"])
+                    ev_type = ev["Type"]
+                    pos = get_ev_fev_timedelta(ev).total_seconds()
+                    if ev_type == "STARTED_SPEAKING":
+                        pos = pos - 1.5
+                    plt.axvline(pos)  # , label="pyplot vertical line")
+                    plt.text(
+                        pos,
+                        0.2,
+                        f"event:{ev_type}:{ev['Msg']}",
+                        rotation=90,
+                        horizontalalignment="left"
+                        if ev_type != "STOPPED_SPEAKING"
+                        else "right",
+                        verticalalignment="center",
+                    )
+            plt.title("Monophonic")
+            plt.savefig(file_path, format="png")
+
+        data_points = get_data_points(utter_events)
+
+        return {
+            "wav_path": saved_wav_path,
+            "num_samples": len(utter_events) // 3,
+            "meta": call_obj,
+            "data_points": data_points,
+        }
+
+    def retrieve_callmeta(uri):
+        cid = Path(urlsplit(uri).path).stem
+        meta = mongo_collection.find_one({"SystemID": cid})
+        duration = meta["EndTS"] - meta["StartTS"]
+        process_meta = process_call(meta)
+        return {"url": uri, "meta": meta, "duration": duration, "process": process_meta}
+
+    # @plot_app.command()
+    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
+
+        # plot_call(retrieve_callmeta("http://saasdev.agaralabs.com/calls/JOR9V47L03AGUEL"))
+        call_lens = lens["users"].Each()["calls"].Each()
+        call_stats = call_lens.modify(retrieve_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():
+        def gen_data_values(saved_wav_path, data_points):
+            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, code = dp["start_time"], dp["end_time"], dp["code"]
+                code_seg = call_seg[start * 1000 : end * 1000]
+                code_fb = BytesIO()
+                code_seg.export(code_fb, format="wav")
+                code_wav = code_fb.getvalue()
+                # import pdb; pdb.set_trace()
+                yield code, code_seg.duration_seconds, code_wav
+
+        call_lens = lens["users"].Each()["calls"].Each()
+        call_stats = call_lens.modify(retrieve_callmeta)(call_logs)
+        call_objs = call_lens.collect()(call_stats)
+
+        def data_source():
+            for call_obj in tqdm(call_objs):
+                saved_wav_path, data_points, sys_id = (
+                    call_obj["process"]["wav_path"],
+                    call_obj["process"]["data_points"],
+                    call_obj["meta"]["SystemID"],
+                )
+                for dp in gen_data_values(saved_wav_path, data_points):
+                    yield dp
+
+        asr_data_writer(call_asr_data, "call_alphanum", data_source())
+
+    # @leader_app.command()
+    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)
+            rel_dur = relativedelta(
+                seconds=int(n_duration.total_seconds()),
+                microseconds=n_duration.microseconds,
+            )
+            return {
+                "num_samples": n_samples,
+                "duration": n_duration.total_seconds(),
+                "samples_rate": n_samples / n_duration.total_seconds(),
+                "duration_str": f"{rel_dur.minutes} mins {rel_dur.seconds} secs",
+                "name": call_stat["name"],
+            }
+
+        call_lens = lens["users"].Each()["calls"].Each()
+        call_stats = call_lens.modify(retrieve_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": "Codes",
+                "name": "Name",
+                "samples_rate": "SpeechRate",
+                "duration_str": "Duration",
+            }
+        )[["Rank", "Name", "Codes", "Duration"]]
+        print(
+            """Today's ASR Speller Dataset Leaderboard:
+----------------------------------------"""
+        )
+        print(leader_board.to_string(index=False))
+
+    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_utils/generator.py

@@ -4,7 +4,7 @@
 import argparse
 import logging
 from pathlib import Path
-from .utils import random_pnr_generator, manifest_str
+from .utils import random_pnr_generator, asr_data_writer
 from .tts.googletts import GoogleTTS
 from tqdm import tqdm
 import random
@@ -15,27 +15,21 @@ logging.basicConfig(
 logger = logging.getLogger(__name__)
 
 
-def generate_asr_data(output_dir, count):
+def pnr_tts_streamer(count):
     google_voices = GoogleTTS.voice_list()
     gtts = GoogleTTS()
-    wav_dir = output_dir / Path("pnr_data")
+    for pnr_code in tqdm(random_pnr_generator(count)):
-    wav_dir.mkdir(parents=True, exist_ok=True)
+        tts_code = f'<speak><say-as interpret-as="verbatim">{pnr_code}</say-as></speak>'
-    asr_manifest = output_dir / Path("pnr_data").with_suffix(".json")
+        param = random.choice(google_voices)
-    with asr_manifest.open("w") as mf:
+        param["sample_rate"] = 24000
-        for pnr_code in tqdm(random_pnr_generator(count)):
+        param["num_channels"] = 1
-            tts_code = (
+        wav_data = gtts.text_to_speech(text=tts_code, params=param)
-                f'<speak><say-as interpret-as="verbatim">{pnr_code}</say-as></speak>'
+        audio_dur = len(wav_data[44:]) / (2 * 24000)
-            )
+        yield pnr_code, audio_dur, wav_data
-            param = random.choice(google_voices)
+
-            param["sample_rate"] = 24000
+
-            param["num_channels"] = 1
+def generate_asr_data_fromtts(output_dir, dataset_name, count):
-            wav_data = gtts.text_to_speech(text=tts_code, params=param)
+    asr_data_writer(output_dir, dataset_name, pnr_tts_streamer(count))
-            audio_dur = len(wav_data[44:]) / (2 * 24000)
-            pnr_af = wav_dir / Path(pnr_code).with_suffix(".wav")
-            pnr_af.write_bytes(wav_data)
-            rel_pnr_path = pnr_af.relative_to(output_dir)
-            manifest = manifest_str(str(rel_pnr_path), audio_dur, pnr_code)
-            mf.write(manifest)
 
 
 def arg_parser():
@@ -52,13 +46,16 @@ def arg_parser():
     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(**vars(args))
+    generate_asr_data_fromtts(**vars(args))
 
 
 if __name__ == "__main__":

jasper/data_utils/process.py

@@ -1,9 +1,13 @@
 import json
 from pathlib import Path
 from sklearn.model_selection import train_test_split
-from num2words import num2words
+from .utils import alnum_to_asr_tokens
+import typer
+
+app = typer.Typer()
 
 
+@app.command()
 def separate_space_convert_digit_setpath():
     with Path("/home/malar/work/asr-data-utils/asr_data/pnr_data.json").open("r") as pf:
         pnr_jsonl = pf.readlines()
@@ -12,9 +16,7 @@ def separate_space_convert_digit_setpath():
 
     new_pnr_data = []
     for i in pnr_data:
-        letters = " ".join(list(i["text"]))
+        i["text"] = alnum_to_asr_tokens(i["text"])
-        num_tokens = [num2words(c) if "0" <= c <= "9" else c for c in letters]
-        i["text"] = ("".join(num_tokens)).lower()
         i["audio_filepath"] = i["audio_filepath"].replace(
             "pnr_data/", "/dataset/asr_data/pnr_data/wav/"
         )
@@ -27,24 +29,39 @@ def separate_space_convert_digit_setpath():
         pf.write(new_pnr_data)
 
 
-separate_space_convert_digit_setpath()
+@app.command()
-
+def split_data(manifest_path: Path = Path("/dataset/asr_data/pnr_data/pnr_data.json")):
-
+    with manifest_path.open("r") as pf:
-def split_data():
-    with Path("/dataset/asr_data/pnr_data/pnr_data.json").open("r") as pf:
         pnr_jsonl = pf.readlines()
     train_pnr, test_pnr = train_test_split(pnr_jsonl, test_size=0.1)
-    with Path("/dataset/asr_data/pnr_data/train_manifest.json").open("w") as pf:
+    with (manifest_path.parent / Path("train_manifest.json")).open("w") as pf:
         pnr_data = "".join(train_pnr)
         pf.write(pnr_data)
-    with Path("/dataset/asr_data/pnr_data/test_manifest.json").open("w") as pf:
+    with (manifest_path.parent / Path("test_manifest.json")).open("w") as pf:
         pnr_data = "".join(test_pnr)
         pf.write(pnr_data)
 
 
-split_data()
+@app.command()
+def fix_path(
+    dataset_path: Path = Path("/dataset/asr_data/call_alphanum"),
+):
+    manifest_path = dataset_path / Path('manifest.json')
+    with manifest_path.open("r") as pf:
+        pnr_jsonl = pf.readlines()
+        pnr_data = [json.loads(i) for i in pnr_jsonl]
+        new_pnr_data = []
+        for i in pnr_data:
+            i["audio_filepath"] = str(dataset_path / Path(i["audio_filepath"]))
+            new_pnr_data.append(i)
+        new_pnr_jsonl = [json.dumps(i) for i in new_pnr_data]
+        real_manifest_path = dataset_path / Path('real_manifest.json')
+        with real_manifest_path.open("w") as pf:
+            new_pnr_data = "\n".join(new_pnr_jsonl)  # + "\n"
+            pf.write(new_pnr_data)
 
 
+@app.command()
 def augment_an4():
     an4_train = Path("/dataset/asr_data/an4/train_manifest.json").read_bytes()
     an4_test = Path("/dataset/asr_data/an4/test_manifest.json").read_bytes()
@@ -57,10 +74,11 @@ def augment_an4():
         pf.write(an4_test + pnr_test)
 
 
-augment_an4()
+# augment_an4()
 
 
-def validate_data(data_file):
+@app.command()
+def validate_data(data_file: Path = Path("/dataset/asr_data/call_alphanum/train_manifest.json")):
     with Path(data_file).open("r") as pf:
         pnr_jsonl = pf.readlines()
     for (i, s) in enumerate(pnr_jsonl):
@@ -70,10 +88,13 @@ def validate_data(data_file):
             print(f"failed on {i}")
 
 
-validate_data("/dataset/asr_data/an4_pnr/test_manifest.json")
+def main():
-validate_data("/dataset/asr_data/an4_pnr/train_manifest.json")
+    app()
 
 
+if __name__ == "__main__":
+    main()
+
 # def convert_digits(data_file="/dataset/asr_data/an4_pnr/test_manifest.json"):
 #     with Path(data_file).open("r") as pf:
 #         pnr_jsonl = pf.readlines()

jasper/data_utils/utils.py

@@ -2,6 +2,8 @@ import numpy as np
 import wave
 import io
 import json
+from pathlib import Path
+from num2words import num2words
 
 
 def manifest_str(path, dur, text):
@@ -38,6 +40,27 @@ def random_pnr_generator(count=10000):
     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 asr_data_writer(output_dir, dataset_name, asr_data_source):
+    dataset_dir = output_dir / Path(dataset_name)
+    (dataset_dir / Path("wav")).mkdir(parents=True, exist_ok=True)
+    asr_manifest = dataset_dir / Path("manifest.json")
+    with asr_manifest.open("w") as mf:
+        for pnr_code, audio_dur, wav_data in asr_data_source:
+            pnr_af = dataset_dir / Path("wav") / Path(pnr_code).with_suffix(".wav")
+            pnr_af.write_bytes(wav_data)
+            rel_pnr_path = pnr_af.relative_to(dataset_dir)
+            manifest = manifest_str(
+                str(rel_pnr_path), audio_dur, alnum_to_asr_tokens(pnr_code)
+            )
+            mf.write(manifest)
+
+
 def main():
     for c in random_pnr_generator():
         print(c)

jasper/train.py

@@ -82,8 +82,7 @@ def parse_args():
     )
 
     args = parser.parse_args()
-
+    if args.max_steps is None and args.num_epochs is None:
-    if args.max_steps is not None and args.num_epochs is not None:
         raise ValueError("Either max_steps or num_epochs should be provided.")
     return args
 
@@ -311,7 +310,6 @@ def main():
 
     # build dags
     train_loss, callbacks, steps_per_epoch = create_all_dags(args, neural_factory)
-
     # train model
     neural_factory.train(
         tensors_to_optimize=[train_loss],

setup.py

@@ -2,6 +2,8 @@ from setuptools import setup, find_packages
 
 requirements = [
     "ruamel.yaml",
+    "torch==1.4.0",
+    "torchvision==0.5.0",
     "nemo_toolkit[asr] @ git+https://github.com/NVIDIA/NeMo.git@09e3ba4dfe333f86d6c5c1048e07210924294be9#egg=nemo_toolkit",
 ]
 
@@ -14,7 +16,19 @@ extra_requirements = {
         "scikit_learn~=0.22.1",
         "pandas~=1.0.3",
         "boto3~=1.12.35",
+        "ruamel.yaml==0.16.10",
+        "pymongo==3.10.1",
+        "librosa==0.7.2",
+        "matplotlib==3.2.1",
+        "pandas==1.0.3",
+        "tabulate==0.8.7",
+        "typer[all]==0.1.1",
+        "lenses @ git+https://github.com/ingolemo/python-lenses.git@b2a2a9aa5b61540992d70b2cf36008d0121e8948#egg=lenses",
     ],
+    # "train": [
+    #     "torchaudio==0.5.0",
+    #     "torch-stft==0.1.4",
+    # ]
 }
 packages = find_packages()
 
@@ -35,6 +49,8 @@ setup(
             "jasper_asr_rpyc_server = jasper.server:main",
             "jasper_asr_trainer = jasper.train:main",
             "jasper_asr_data_generate = jasper.data_utils.generator:main",
+            "jasper_asr_data_recycle = jasper.data_utils.call_recycler:main",
+            "jasper_asr_data_preprocess = jasper.data_utils.process:main",
         ]
     },
     zip_safe=False,