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from faster_whisper import WhisperModel |
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from tqdm import tqdm |
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import os |
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from App.Transcription.Schemas import TranscriptionMetadata |
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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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metadata.logs = "STARTING" |
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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(file_path, beam_size=5) |
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total_duration = round(info.duration, 2) |
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metadata.logs = ( |
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"Detected language '%s' with probability %f" |
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% (info.language, info.language_probability), |
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) |
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metadata.language = info.language |
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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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segment_duration = segment.end - segment.start |
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time_stamp = "[%.2fs -> %.2fs]" % (segment.start, segment.end) |
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result[time_stamp] = segment.text |
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metadata.logs = "Transcribing.." |
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metadata.percentage = f"{((segment.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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metadata.logs = f"Falied error {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 |
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os.remove(file_path) |
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return result |
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