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f5c49338d9
...
ae5586be72
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@ -2,10 +2,6 @@ import os
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import logging
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import rpyc
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from functools import lru_cache
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import typer
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from pathlib import Path
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app = typer.Typer()
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logging.basicConfig(
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level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
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@ -23,28 +19,3 @@ def transcribe_gen(asr_host=ASR_HOST, asr_port=ASR_PORT):
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asr = rpyc.connect(asr_host, asr_port).root
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logger.info(f"connected to asr server successfully")
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return asr.transcribe
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@app.command()
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def transcribe_file(audio_file: Path):
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from pydub import AudioSegment
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transcriber = transcribe_gen()
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aud_seg = (
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AudioSegment.from_file_using_temporary_files(audio_file)
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.set_channels(1)
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.set_sample_width(2)
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.set_frame_rate(24000)
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)
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tscript_file_path = audio_file.with_suffix(".txt")
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transcription = transcriber(aud_seg.raw_data)
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with open(tscript_file_path, "w") as tf:
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tf.write(transcription)
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def main():
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app()
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if __name__ == "__main__":
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main()
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@ -1,16 +1,13 @@
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import io
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import os
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import re
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import json
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import base64
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import wave
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from pathlib import Path
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from itertools import product
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from functools import partial
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from math import floor
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from uuid import uuid4
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from urllib.parse import urlsplit, urlencode
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from urllib.request import Request, urlopen
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from urllib.parse import urlsplit
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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@ -103,9 +100,6 @@ def ui_dump_manifest_writer(output_dir, dataset_name, asr_data_source, verbose=F
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"data": [],
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}
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data_funcs = []
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deepgram_transcriber = deepgram_transcribe_gen()
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# t2n = Text2Num()
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transcriber_gcp = gcp_transcribe_gen()
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transcriber_pretrained = transcribe_gen(asr_port=8044)
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with asr_manifest.open("w") as mf:
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@ -125,10 +119,6 @@ def ui_dump_manifest_writer(output_dir, dataset_name, asr_data_source, verbose=F
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pretrained_result = transcriber_pretrained(aud_seg.raw_data)
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gcp_seg = aud_seg.set_frame_rate(16000)
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gcp_result = transcriber_gcp(gcp_seg.raw_data)
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aud_data = audio_path.read_bytes()
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dgram_result = deepgram_transcriber(aud_data)
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# gtruth = dp['text']
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# dgram_result = t2n.convert(dgram_script)
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pretrained_wer = word_error_rate([transcript], [pretrained_result])
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wav_plot_path = (
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dataset_dir / Path("wav_plots") / Path(fname).with_suffix(".png")
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@ -145,7 +135,6 @@ def ui_dump_manifest_writer(output_dir, dataset_name, asr_data_source, verbose=F
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"caller": caller_name,
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"utterance_id": fname,
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"gcp_asr": gcp_result,
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"deepgram_asr": dgram_result,
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"pretrained_asr": pretrained_result,
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"pretrained_wer": pretrained_wer,
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"plot_path": str(wav_plot_path),
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@ -236,12 +225,6 @@ class ExtendedPath(type(Path())):
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with self.open("r") as jf:
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return json.load(jf)
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def read_jsonl(self):
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print(f"reading jsonl from {self}")
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with self.open("r") as jf:
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for l in jf.readlines():
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yield json.loads(l)
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def write_json(self, data):
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print(f"writing json to {self}")
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self.parent.mkdir(parents=True, exist_ok=True)
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@ -482,203 +465,6 @@ def gcp_transcribe_gen():
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return sample_recognize
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def deepgram_transcribe_gen():
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DEEPGRAM_URL = "https://brain.deepgram.com/v2/listen"
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MODEL = "agara"
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encoding = "linear16"
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sample_rate = "8000"
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# diarize = "false"
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q_params = {
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"model": MODEL,
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"encoding": encoding,
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"sample_rate": sample_rate,
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"language": "en-US",
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"multichannel": "false",
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"punctuate": "true",
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}
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url = "{}?{}".format(DEEPGRAM_URL, urlencode(q_params))
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# print(url)
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creds = ("arjun@agaralabs.com", "PoX1Y@x4h%oS")
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def deepgram_offline(audio_data):
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request = Request(
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url,
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method="POST",
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headers={
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"Authorization": "Basic {}".format(
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base64.b64encode("{}:{}".format(*creds).encode("utf-8")).decode(
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"utf-8"
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)
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)
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},
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data=audio_data,
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)
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with urlopen(request) as response:
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msg = json.loads(response.read())
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data = msg["results"]["channels"][0]["alternatives"][0]
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return data["transcript"]
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return deepgram_offline
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class Text2Num(object):
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"""docstring for Text2Num."""
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def __init__(self):
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numwords = {}
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if not numwords:
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units = [
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"zero",
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"one",
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"two",
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"three",
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"four",
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"five",
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"six",
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"seven",
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"eight",
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"nine",
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"ten",
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"eleven",
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"twelve",
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"thirteen",
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"fourteen",
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"fifteen",
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"sixteen",
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"seventeen",
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"eighteen",
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"nineteen",
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]
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tens = [
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"",
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"",
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"twenty",
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"thirty",
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"forty",
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"fifty",
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"sixty",
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"seventy",
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"eighty",
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"ninety",
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]
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scales = ["hundred", "thousand", "million", "billion", "trillion"]
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numwords["and"] = (1, 0)
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for idx, word in enumerate(units):
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numwords[word] = (1, idx)
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for idx, word in enumerate(tens):
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numwords[word] = (1, idx * 10)
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for idx, word in enumerate(scales):
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numwords[word] = (10 ** (idx * 3 or 2), 0)
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self.numwords = numwords
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def is_num(self, word):
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return word in self.numwords
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def parseOrdinal(self, utterance, **kwargs):
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lookup_dict = {
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"first": 1,
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"second": 2,
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"third": 3,
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"fourth": 4,
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"fifth": 5,
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"sixth": 6,
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"seventh": 7,
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"eighth": 8,
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"ninth": 9,
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"tenth": 10,
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"one": 1,
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"two": 2,
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"three": 3,
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"four": 4,
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"five": 5,
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"six": 6,
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"seven": 7,
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"eight": 8,
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"nine": 9,
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"ten": 10,
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"1": 1,
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"2": 2,
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"3": 3,
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"4": 4,
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"5": 5,
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"6": 6,
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"7": 7,
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"8": 8,
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"9": 9,
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"10": 10,
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"last": -1,
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}
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pattern = re.compile(
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r"(\s|^)(?P<num>(first)|(third)|(fourth)|(fifth)|(sixth)|(seventh)|(eighth)|(ninth)|(tenth)|(two)|(three)|(four)|(five)|(six)|(seven)|(eight)|(nine)|(ten)|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|(last))(\s|$)",
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re.IGNORECASE,
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)
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ordinal = ""
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if pattern.search(utterance):
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ordinal = pattern.search(utterance).groupdict()["num"].strip()
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elif re.search(r"(\s|^)(?P<num>(second))(\s|$)", utterance):
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ordinal = "second"
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elif re.search(r"(\s|^)(?P<num>(one))(\s|$)", utterance):
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ordinal = "one"
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ordinal = lookup_dict.get(ordinal, "")
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return ordinal
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def convert(self, sent):
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# res = []
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# for token in sent.split():
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# if token in self.numwords:
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# res.append(str(self.text2int(token)))
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# else:
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# res.append(token)
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# return " ".join(res)
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return " ".join(
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[
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str(self.parseOrdinal(x)) if self.parseOrdinal(x) != "" else x
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for x in sent.split()
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]
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)
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def text2int(self, textnum):
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current = result = 0
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for word in textnum.split():
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if word not in self.numwords:
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raise Exception("Illegal word: " + word)
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scale, increment = self.numwords[word]
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current = current * scale + increment
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if scale > 100:
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result += current
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current = 0
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return result + current
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def is_sub_sequence(str1, str2):
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m = len(str1)
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n = len(str2)
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def check_seq(string1, string2, m, n):
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# Base Cases
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if m == 0:
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return True
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if n == 0:
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return False
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# If last characters of two strings are matching
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if string1[m - 1] == string2[n - 1]:
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return check_seq(string1, string2, m - 1, n - 1)
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# If last characters are not matching
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return check_seq(string1, string2, m, n - 1)
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return check_seq(str1, str2, m, n)
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def parallel_apply(fn, iterable, workers=8):
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with ThreadPoolExecutor(max_workers=workers) as exe:
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print(f"parallelly applying {fn}")
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@ -156,47 +156,6 @@ def sample_ui(
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ExtendedPath(sample_path).write_json(processed_data)
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@app.command()
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def sample_asr_accuracy(
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data_name: str = typer.Option(
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"png_06_2020_week1_numbers_window_customer", show_default=True
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),
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dump_dir: Path = Path("./data/asr_data"),
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sample_file: Path = Path("sample_dump.json"),
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asr_service: str = "deepgram",
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):
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# import pandas as pd
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# from pydub import AudioSegment
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from ..utils import is_sub_sequence, Text2Num
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# from ..utils import deepgram_transcribe_gen
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#
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# deepgram_transcriber = deepgram_transcribe_gen()
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t2n = Text2Num()
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# processed_data_path = dump_dir / Path(data_name) / dump_file
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sample_path = dump_dir / Path(data_name) / sample_file
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processed_data = ExtendedPath(sample_path).read_json()
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# asr_data = []
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match_count, total_samples = 0, len(processed_data["data"])
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for dp in tqdm(processed_data["data"]):
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# aud_data = Path(dp["audio_path"]).read_bytes()
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# dgram_result = deepgram_transcriber(aud_data)
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# dp["deepgram_asr"] = dgram_result
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gcp_num = dp["text"]
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dgm_num = t2n.convert(dp["deepgram_asr"].lower())
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if is_sub_sequence(gcp_num, dgm_num):
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match_count += 1
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print(f"MATCH GCP:{gcp_num}\tDGM:{dgm_num}")
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else:
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print(f"FAIL GCP:{gcp_num}\tDGM:{dgm_num}")
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# asr_data.append(dp)
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typer.echo(
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f"{match_count} from deepgram matches with {total_samples} gcp transcripts."
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)
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# processed_data["data"] = asr_data
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# ExtendedPath(sample_path).write_json(processed_data)
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@app.command()
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def task_ui(
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data_name: str = typer.Option("call_upwork_train_cnd", show_default=True),
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@ -231,9 +190,7 @@ def dump_corrections(
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col = get_mongo_conn(col="asr_validation")
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task_id = [c for c in col.distinct("task_id") if c.rsplit("-", 1)[1] == task_uid][0]
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corrections = list(col.find({"type": "correction"}, projection={"_id": False}))
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cursor_obj = col.find(
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{"type": "correction", "task_id": task_id}, projection={"_id": False}
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)
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cursor_obj = col.find({"type": "correction", "task_id": task_id}, projection={"_id": False})
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corrections = [c for c in cursor_obj]
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ExtendedPath(dump_path).write_json(corrections)
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@ -314,9 +271,7 @@ def split_extract(
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dump_file: Path = Path("ui_dump.json"),
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manifest_file: Path = Path("manifest.json"),
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corrections_file: str = typer.Option("corrections.json", show_default=True),
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conv_data_path: Path = typer.Option(
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Path("./data/conv_data.json"), show_default=True
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),
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conv_data_path: Path = typer.Option(Path("./data/conv_data.json"), show_default=True),
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extraction_type: ExtractionType = ExtractionType.all,
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):
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import shutil
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@ -338,9 +293,7 @@ def split_extract(
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def extract_manifest(mg):
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for m in mg:
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if m["text"] in extraction_vals:
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shutil.copy(
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m["audio_path"], dest_data_dir / Path(m["audio_filepath"])
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)
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shutil.copy(m["audio_path"], dest_data_dir / Path(m["audio_filepath"]))
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yield m
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asr_manifest_writer(dest_manifest_path, extract_manifest(manifest_gen))
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@ -349,14 +302,12 @@ def split_extract(
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orig_ui_data = ExtendedPath(ui_data_path).read_json()
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ui_data = orig_ui_data["data"]
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file_ui_map = {Path(u["audio_filepath"]).stem: u for u in ui_data}
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extracted_ui_data = list(
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filter(lambda u: u["text"] in extraction_vals, ui_data)
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)
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extracted_ui_data = list(filter(lambda u: u["text"] in extraction_vals, ui_data))
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final_data = []
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for i, d in enumerate(extracted_ui_data):
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d["real_idx"] = i
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d['real_idx'] = i
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final_data.append(d)
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orig_ui_data["data"] = final_data
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orig_ui_data['data'] = final_data
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ExtendedPath(dest_ui_path).write_json(orig_ui_data)
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if corrections_file:
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|
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@ -372,7 +323,7 @@ def split_extract(
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)
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ExtendedPath(dest_correction_path).write_json(extracted_corrections)
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if extraction_type.value == "all":
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if extraction_type.value == 'all':
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for ext_key in conv_data.keys():
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extract_data_of_type(ext_key)
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else:
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|
|
@ -394,7 +345,7 @@ def update_corrections(
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def correct_manifest(ui_dump_path, corrections_path):
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corrections = ExtendedPath(corrections_path).read_json()
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ui_data = ExtendedPath(ui_dump_path).read_json()["data"]
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ui_data = ExtendedPath(ui_dump_path).read_json()['data']
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correct_set = {
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c["code"] for c in corrections if c["value"]["status"] == "Correct"
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}
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|
|
@ -423,9 +374,7 @@ def update_corrections(
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)
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else:
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orig_audio_path = Path(d["audio_path"])
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new_name = str(
|
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Path(tscript_uuid_fname(correct_text)).with_suffix(".wav")
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)
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new_name = str(Path(tscript_uuid_fname(correct_text)).with_suffix(".wav"))
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new_audio_path = orig_audio_path.with_name(new_name)
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orig_audio_path.replace(new_audio_path)
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new_filepath = str(Path(d["audio_filepath"]).with_name(new_name))
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|
|
|
|||
|
|
@ -1,58 +0,0 @@
|
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from pathlib import Path
|
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|
||||
import streamlit as st
|
||||
import typer
|
||||
from jasper.data.utils import ExtendedPath
|
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from jasper.data.validation.st_rerun import rerun
|
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|
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app = typer.Typer()
|
||||
|
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if not hasattr(st, "mongo_connected"):
|
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# st.task_id = str(uuid4())
|
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task_path = ExtendedPath("preview.lck")
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||||
|
||||
def current_cursor_fn():
|
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return task_path.read_json()["current_cursor"]
|
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|
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def update_cursor_fn(val=0):
|
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task_path.write_json({"current_cursor": val})
|
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rerun()
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||||
|
||||
st.get_current_cursor = current_cursor_fn
|
||||
st.update_cursor = update_cursor_fn
|
||||
st.mongo_connected = True
|
||||
# cursor_obj = mongo_conn.find_one({"type": "current_cursor", "task_id": st.task_id})
|
||||
# if not cursor_obj:
|
||||
update_cursor_fn(0)
|
||||
|
||||
|
||||
@st.cache()
|
||||
def load_ui_data(validation_ui_data_path: Path):
|
||||
typer.echo(f"Using validation ui data from {validation_ui_data_path}")
|
||||
return list(ExtendedPath(validation_ui_data_path).read_jsonl())
|
||||
|
||||
|
||||
@app.command()
|
||||
def main(manifest: Path):
|
||||
asr_data = load_ui_data(manifest)
|
||||
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]
|
||||
st.title(f"ASR Manifest Preview")
|
||||
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)
|
||||
)
|
||||
if new_sample != sample_no + 1:
|
||||
st.update_cursor(new_sample - 1)
|
||||
st.sidebar.markdown(f"Gold Text: **{sample['text']}**")
|
||||
st.audio(Path(sample["audio_filepath"]).open("rb"))
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
app()
|
||||
except SystemExit:
|
||||
pass
|
||||
Loading…
Reference in New Issue