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from faster_whisper import WhisperModel |
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from datetime import datetime, timedelta |
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from tqdm import tqdm |
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import os, time |
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from App.Transcription.Schemas import TranscriptionMetadata, TranscriptionResult |
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current_time = time.localtime() |
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model_size = "tiny" |
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def transcribe_file(state, file_path, model_size="tiny"): |
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result = [] |
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metadata = TranscriptionMetadata() |
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start_time = datetime.now() |
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state.update_state( |
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state="PROGRESS", |
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meta=metadata.dict(), |
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) |
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model = WhisperModel(model_size, device="cpu", compute_type="int8") |
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segments, info = model.transcribe( |
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file_path, beam_size=5, vad_filter=True, word_timestamps=True |
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) |
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total_duration = round(info.duration, 2) |
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metadata.language = info.language |
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metadata.duration = int(total_duration) |
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state.update_state( |
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state="PROGRESS", |
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meta=metadata.dict(), |
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) |
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try: |
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with tqdm(total=total_duration, unit=" seconds") as pbar: |
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for segment in segments: |
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for word in segment.words: |
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segment_duration = word.end - word.start |
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temp = { |
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"start": word.start, |
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"end": word.end, |
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"text": word.word, |
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} |
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result.append(temp) |
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time_difference = datetime.now() - start_time |
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if time_difference >= timedelta(seconds=30): |
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start_time = datetime.now() |
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metadata.percentage = f"{((word.end / total_duration)*100)}" |
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state.update_state(state="PROGRESS", meta=metadata.dict()) |
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pbar.update(segment_duration) |
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except Exception as e: |
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state.update_state( |
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state="FAILED", |
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meta=metadata.dict(), |
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) |
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os.remove(file_path) |
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return f"Falied error {e}" |
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os.remove(file_path) |
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transcription_result = TranscriptionResult(**metadata.dict()) |
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transcription_result.transcript = result |
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return transcription_result.dict() |
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