| |
| """Preenche as transcricoes vazias do metadata.csv usando Whisper. |
| |
| Uso (na pasta fala_pb, na sua maquina com whisper instalado): |
| python transcribe_missing.py # modelo small |
| python transcribe_missing.py --model medium |
| |
| Requer: pip install openai-whisper (voce ja usou no process_dataset.py) |
| Retomavel: salva progresso em transcricoes_parciais.csv a cada arquivo; |
| se interromper, rode de novo que continua de onde parou. |
| """ |
| import argparse |
| import csv |
| import os |
| import re |
| import sys |
| import unicodedata |
|
|
| csv.field_size_limit(10_000_000) |
| BASE = os.path.dirname(os.path.abspath(__file__)) |
| META = os.path.join(BASE, 'metadata.csv') |
| PARTIAL = os.path.join(BASE, 'transcricoes_parciais.csv') |
|
|
|
|
| def norm_text(t): |
| if not t: |
| return '' |
| t = t.replace('...', ' ').replace('…', ' ') |
| t = re.sub(r'[^\w\sÀ-ÿ]', ' ', t, flags=re.UNICODE) |
| return re.sub(r'\s+', ' ', t).strip().lower() |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument('--model', default='small') |
| args = ap.parse_args() |
|
|
| rows = list(csv.DictReader(open(META, encoding='utf-8'))) |
| pending = [r for r in rows if not r['text'].strip()] |
| print(f'{len(pending)} audios sem transcricao') |
| if not pending: |
| return |
|
|
| done = {} |
| if os.path.exists(PARTIAL): |
| for r in csv.DictReader(open(PARTIAL, encoding='utf-8')): |
| done[r['id']] = r['text'] |
| print(f'{len(done)} ja transcritos anteriormente') |
|
|
| todo = [r for r in pending if r['id'] not in done] |
| if todo: |
| import whisper |
| model = whisper.load_model(args.model) |
| new_file = not os.path.exists(PARTIAL) |
| with open(PARTIAL, 'a', newline='', encoding='utf-8') as pf: |
| w = csv.writer(pf) |
| if new_file: |
| w.writerow(['id', 'text']) |
| for i, r in enumerate(todo, 1): |
| path = os.path.join(BASE, r['file_name'].replace('/', os.sep)) |
| try: |
| res = model.transcribe(path, language='pt') |
| text = res['text'].strip() |
| except Exception as e: |
| print(f'ERRO {r["id"]}: {e}') |
| text = '' |
| done[r['id']] = text |
| w.writerow([r['id'], text]) |
| pf.flush() |
| print(f'[{i}/{len(todo)}] {r["id"]}: {text[:70]}') |
|
|
| |
| for r in rows: |
| if r['id'] in done and done[r['id']]: |
| r['text'] = done[r['id']] |
| r['normalized_text'] = norm_text(done[r['id']]) |
| r['notes'] = (r['notes'].replace( |
| 'transcricao pendente (transcribe_missing.py)', |
| 'transcricao automatica (Whisper, revisao manual recomendada)')) |
| tmp = META + '.tmp' |
| with open(tmp, 'w', newline='', encoding='utf-8') as f: |
| w = csv.DictWriter(f, fieldnames=rows[0].keys()) |
| w.writeheader() |
| w.writerows(rows) |
| os.replace(tmp, META) |
| print(f'metadata.csv atualizado. Pode apagar {os.path.basename(PARTIAL)}.') |
|
|
|
|
| if __name__ == '__main__': |
| main() |
|
|
