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| from __future__ import annotations | |
| DIARIZATION_MODELS: dict[str, dict] = { | |
| "pyannote-3.1": { | |
| "name": "pyannote speaker-diarization 3.1", | |
| "repo_id": "pyannote/speaker-diarization-3.1", | |
| "backend": "pyannote", | |
| }, | |
| "nemo-msdd": { | |
| "name": "NeMo MSDD (экспериментальный)", | |
| "backend": "nemo-msdd", | |
| "config": "diar_msdd_telephonic", | |
| }, | |
| } | |
| TRANSCRIPTION_MODELS: dict[str, dict] = { | |
| # --- faster-whisper (CTranslate2) --- | |
| "whisper-large-v3": { | |
| "name": "Whisper large-v3", | |
| "repo_id": "Systran/faster-whisper-large-v3", | |
| "backend": "faster-whisper", | |
| "compute_type": "float16", | |
| "language": "ru", | |
| }, | |
| "whisper-large-v3-turbo": { | |
| "name": "Whisper large-v3-turbo", | |
| "repo_id": "Systran/faster-whisper-large-v3-turbo", | |
| "backend": "faster-whisper", | |
| "compute_type": "float16", | |
| "language": "ru", | |
| }, | |
| # --- transformers-whisper (HF native, Russian fine-tuned) --- | |
| "whisper-podlodka-turbo": { | |
| "name": "Whisper Podlodka Turbo (RU)", | |
| "repo_id": "bond005/whisper-podlodka-turbo", | |
| "backend": "transformers-whisper", | |
| "language": "ru", | |
| }, | |
| "whisper-large-v3-russian": { | |
| "name": "Whisper large-v3 Russian", | |
| "repo_id": "antony66/whisper-large-v3-russian", | |
| "backend": "transformers-whisper", | |
| "language": "ru", | |
| }, | |
| "whisper-russian-ties-podlodka": { | |
| "name": "Whisper TIES-merge Podlodka (RU)", | |
| "repo_id": "Apel-sin/whisper-large-v3-russian-ties-podlodka-v1.2", | |
| "backend": "transformers-whisper", | |
| "language": "ru", | |
| }, | |
| # --- GigaAM --- | |
| "gigaam-ctc": { | |
| "name": "GigaAM CTC", | |
| "backend": "gigaam", | |
| "model_type": "ctc", | |
| "language": "ru", | |
| }, | |
| "gigaam-rnnt": { | |
| "name": "GigaAM RNNT", | |
| "backend": "gigaam", | |
| "model_type": "rnnt", | |
| "language": "ru", | |
| }, | |
| } | |
| def diarization_choices() -> list[tuple[str, str]]: | |
| return [(v["name"], k) for k, v in DIARIZATION_MODELS.items()] | |
| def transcription_choices() -> list[tuple[str, str]]: | |
| return [(v["name"], k) for k, v in TRANSCRIPTION_MODELS.items()] | |