verbanote-server/verbanote/process.py

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import os
import re
import json
from dataclasses import dataclass
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from pathlib import Path
from pyannote.audio import Pipeline
from pydub import AudioSegment
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from whisper import Whisper
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MILLISECONDS_TO_SPACE = 2000
@dataclass
class TxtTranscription:
text: str
file: Path
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def diarize(audiofile: Path, pipeline: Pipeline, output_path: Path) -> Path:
audiofile_prepended = _add_audio_silence(audiofile)
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DIARIZE_FILE = {"uri": "not-important", "audio": audiofile_prepended}
dz = pipeline(DIARIZE_FILE)
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out_file = Path.joinpath(output_path, "diarization.txt")
with open(out_file, "w") as text_file:
text_file.write(str(dz))
print("Diarized:")
print(*list(dz.itertracks(yield_label=True))[:10], sep="\n")
return out_file
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def transcribe(
model: Whisper,
diarized_groups: list,
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files_path: Path,
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lang: str = "en",
word_timestamps: bool = True,
) -> None:
for i in range(len(diarized_groups)):
f = {Path.joinpath(output_path, str(i))}
audio_f = f"{f}.wav"
json_f = f"{f}.json"
result = model.transcribe(
audio=audio_f, language=lang, word_timestamps=word_timestamps
)
with open(json_f, "w") as outfile:
json.dump(result, outfile, indent=4)
# TODO clean up this mess
def output_txt(diarized_groups: list, transcription_path: Path) -> TxtTranscription:
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txt = list("")
gidx = -1
for g in diarized_groups:
shift = re.findall(r"[0-9]+:[0-9]+:[0-9]+\.[0-9]+", string=g[0])[0]
shift = (
_millisec(shift) - MILLISECONDS_TO_SPACE
) # the start time in the original video
shift = max(shift, 0)
gidx += 1
with open(f"{Path.joinpath(transcription_path, str(gidx))}.json") as f:
captions = json.load(f)["segments"]
if captions:
speaker = g[0].split()[-1]
for c in captions:
txt.append(f"[{speaker}] {c['text']}\n")
txt.append("\n")
output = "".join(txt)
fname = Path.joinpath(transcription_path, "transcription_result.txt")
with open(fname, "w", encoding="utf-8") as file:
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file.write(output)
return TxtTranscription(text=output, file=fname)
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def save_diarized_audio_files(
diarization: Path, audiofile: Path, output_path: Path
) -> list:
groups = _group_speakers(diarization)
_save_individual_audio_files(audiofile, groups, output_path)
return groups
def _add_audio_silence(audiofile) -> Path:
spacermilli = MILLISECONDS_TO_SPACE
spacer = AudioSegment.silent(duration=spacermilli)
audio = AudioSegment.from_wav(audiofile)
audio = spacer.append(audio, crossfade=0)
out_file = Path.joinpath(Path(os.path.dirname(audiofile)), "interview_prepend.wav")
audio.export(out_file, format="wav")
return out_file
def _save_individual_audio_files(
audiofile: Path, groups: list[str], output_path: Path
) -> None:
audio = AudioSegment.from_wav(audiofile)
gidx = -1
for g in groups:
start = re.findall(r"[0-9]+:[0-9]+:[0-9]+\.[0-9]+", string=g[0])[0]
end = re.findall(r"[0-9]+:[0-9]+:[0-9]+\.[0-9]+", string=g[-1])[1]
start = _millisec(start) # - spacermilli
end = _millisec(end) # - spacermilli
gidx += 1
audio[start:end].export(
f"{Path.joinpath(output_path, str(gidx))}.wav", format="wav"
)
def _group_speakers(diarization_file: Path) -> list:
dzs = open(diarization_file).read().splitlines()
groups: list = []
g = []
lastend = 0
for d in dzs:
if g and (g[0].split()[-1] != d.split()[-1]): # same speaker
groups.append(g)
g = []
g.append(d)
end = re.findall(r"[0-9]+:[0-9]+:[0-9]+\.[0-9]+", string=d)[1]
end = _millisec(end)
if lastend > end: # segment engulfed by a previous segment
groups.append(g)
g = []
else:
lastend = end
if g:
groups.append(g)
return groups
def _millisec(timeStr):
spl = timeStr.split(":")
s = (int)((int(spl[0]) * 60 * 60 + int(spl[1]) * 60 + float(spl[2])) * 1000)
return s