File size: 2,714 Bytes
047dcf7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 |
import os
import argparse
from datetime import timedelta
import librosa
import torch
from faster_whisper import WhisperModel
def seconds_to_timestamp(seconds):
"""Convert seconds to VTT timestamp format (HH:MM:SS.mmm)"""
t = timedelta(seconds=seconds)
return str(t)[:-3].rjust(11, '0').replace('.', ',')
def write_vtt(segments, output_path):
with open(output_path, 'w', encoding='utf-8') as f:
f.write("WEBVTT\n\n")
for segment in segments:
start_ts = seconds_to_timestamp(segment.start)
end_ts = seconds_to_timestamp(segment.end)
f.write(f"{start_ts} --> {end_ts}\n{segment.text}\n\n")
def transcribe_audio(model, audio_path, word_timestamps=True, vad_filter=True):
print(f"\nProcessing {audio_path}...")
with torch.no_grad():
audio_data, sr = librosa.load(audio_path, sr=None)
audio_data = librosa.resample(audio_data, orig_sr=sr, target_sr=16000)
segments, _ = model.transcribe(
audio_data,
language='ar',
word_timestamps=word_timestamps,
vad_filter=vad_filter
)
for segment in segments:
if segment.words:
for word in segment.words:
print("[%.2fs -> %.2fs] %s" % (word.start, word.end, word.word))
vtt_path = os.path.splitext(audio_path)[0] + ".vtt"
write_vtt(segments, vtt_path)
print(f"VTT written to: {vtt_path}")
def main():
parser = argparse.ArgumentParser(description="Transcribe audio files using Faster-Whisper.")
parser.add_argument("--model_path", required=True, help="Path to the model directory or file")
parser.add_argument("--audio_dir", required=True, help="Directory containing audio files (wav/mp3)")
parser.add_argument("--word_timestamps", type=bool, default=True, help="Enable word timestamps (default: True)")
parser.add_argument("--vad_filter", type=bool, default=True, help="Enable VAD filtering (default: True)")
args = parser.parse_args()
model = WhisperModel(args.model_path)
for file in os.listdir(args.audio_dir):
if file.endswith(".wav") or file.endswith(".mp3"):
audio_path = os.path.join(args.audio_dir, file)
transcribe_audio(
model,
audio_path,
language="ar",
beam_size=5,
task="transcribe",
word_timestamps=args.word_timestamps,
vad_filter=args.vad_filter,
vad_parameters=dict(min_silence_duration_ms=2000)
)
if __name__ == "__main__":
main()
|