Cesar Mendivil
c22767d3d4
- Updated `srt_to_kokoro.py` to provide a CLI entrypoint with argument parsing. - Enhanced error handling and logging for better user feedback. - Introduced a compatibility layer for legacy scripts. - Added configuration handling via `config.toml` for endpoint and API key. - Improved documentation and comments for clarity. Enhance PipelineOrchestrator with in-process transcriber fallback - Implemented `InProcessTranscriber` to handle transcription using multiple strategies. - Added support for `srt_only` flag to return translated SRT without TTS synthesis. - Improved error handling and logging for transcriber initialization. Add installation and usage documentation - Created `INSTALLATION.md` for detailed setup instructions for CPU and GPU environments. - Added `USAGE.md` with practical examples for common use cases and command-line options. - Included a script for automated installation and environment setup. Implement SRT burning utility - Added `burn_srt.py` to facilitate embedding SRT subtitles into video files using ffmpeg. - Provided command-line options for style and codec customization. Update project configuration management - Introduced `config.py` to centralize configuration loading from `config.toml`. - Ensured that environment variables are not read to avoid implicit overrides. Enhance package management with `pyproject.toml` - Added `pyproject.toml` for modern packaging and dependency management. - Defined optional dependencies for CPU and TTS support. Add smoke test fixture for SRT - Created `smoke_test.srt` as a sample subtitle file for testing purposes. Update requirements and setup configurations - Revised `requirements.txt` and `setup.cfg` for better dependency management and clarity. - Included installation instructions for editable mode and local TTS support.
60 lines
1.7 KiB
Plaintext
60 lines
1.7 KiB
Plaintext
# Dependencias comunes del paquete `whisper_project`.
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# Este fichero está orientado a instalaciones en CPU. Algunas librerías
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# publican ruedas separadas para GPU y CPU (p.ej. `torch`). Para forzar
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# la instalación CPU de PyTorch ejecuta:
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#
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# python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
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#
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# O bien instala todo de una vez usando el índice CPU:
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#
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# python -m pip install --index-url https://download.pytorch.org/whl/cpu -r whisper_project/requirements.txt
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#
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# Luego instala el resto de dependencias con:
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# python -m pip install -r whisper_project/requirements.txt
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#
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# Si deseas GPU en el futuro, instala la rueda apropiada de PyTorch
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# para tu versión de CUDA y, si procede, elimina el uso del índice CPU.
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#
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# Este archivo lista las librerías que el código del proyecto puede usar.
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# Instalar en el virtualenv del proyecto con:
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# python -m pip install -r whisper_project/requirements.txt
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# Core / audio
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torch==2.2.2
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numpy==1.26.4
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ffmpeg-python==0.4.0
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# Transcripción y modelos
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faster-whisper==1.2.0
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transformers==4.34.0
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tokenizers==0.13.3
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sentencepiece==0.1.99
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huggingface-hub==0.16.4
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# Traducción (Marian / transformers)
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sacremoses==0.0.53
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# TTS (opcional: Coqui TTS o fallbacks)
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TTS==0.13.0
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soundfile==0.12.1
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librosa==0.10.0.post2
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pyttsx3==2.90
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# HTTP / utilidades
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requests==2.31.0
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tqdm==4.66.1
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# Dependencias instaladas por `faster-whisper` en algunos entornos
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onnxruntime==1.15.1
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ctranslate2==3.18.0
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av==10.0.0
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coloredlogs==15.0.1
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humanfriendly==10.0
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flatbuffers==23.5.26
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# Nota: estos pins se eligieron para compatibilidad con Python 3.11
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# y uso en CPU. Si prefieres versiones distintas (p.ej. ruedas GPU de
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# `torch`), indícamelo y actualizo los pins o añado instrucciones
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# para instalar desde índices alternativos.
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