thomas.kopp
c7cad4bb2a
- transcription.py: new _transcribe_remote_whispercpp() using /inference endpoint - transcription.py: backend param routes to openai or whispercpp remote path - config.py: whisper.backend default 'openai', alt 'whispercpp' - pipeline.py: passes backend from config to transcribe_file - settings: backend dropdown (OpenAI-compat / whisper.cpp) - SETUP.md: whisper.cpp ROCm build and systemd setup instructions whisper-cpp-server running on beastix :8080 (ROCm0, gfx1030, RX 6800 XT)
129 lines
4.5 KiB
Python
129 lines
4.5 KiB
Python
import asyncio
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import httpx
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from typing import Union
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class TranscriptionEngine:
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_model = None
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def _get_model(self, model_name: str = "large-v3", device: str = "auto"):
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if self._model is None:
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from faster_whisper import WhisperModel
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if device == "auto":
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try:
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self._model = WhisperModel(model_name, device="cuda", compute_type="float16")
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except Exception:
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self._model = WhisperModel(model_name, device="cpu", compute_type="int8")
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else:
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compute = "float16" if device in ("cuda", "rocm") else "int8"
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self._model = WhisperModel(model_name, device=device, compute_type=compute)
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return self._model
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async def transcribe_file(
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self,
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audio_path: str,
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language: str = "de",
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model_name: str = "large-v3",
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device: str = "auto",
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base_url: str = "",
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with_segments: bool = False,
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backend: str = "openai",
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) -> Union[str, list[dict]]:
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if base_url:
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if backend == "whispercpp":
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return await self._transcribe_remote_whispercpp(
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audio_path, language, base_url, with_segments
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)
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return await self._transcribe_remote(
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audio_path, language, model_name, base_url, with_segments
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)
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return await self._transcribe_local(
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audio_path, language, model_name, device, with_segments
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)
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async def _transcribe_remote(
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self,
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audio_path: str,
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language: str,
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model_name: str,
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base_url: str,
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with_segments: bool,
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) -> Union[str, list[dict]]:
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async with httpx.AsyncClient(timeout=300) as client:
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with open(audio_path, "rb") as f:
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data = {"model": model_name, "language": language}
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if with_segments:
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data["timestamp_granularities[]"] = "segment"
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data["response_format"] = "verbose_json"
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r = await client.post(
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f"{base_url}/v1/audio/transcriptions",
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files={"file": ("audio.wav", f, "audio/wav")},
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data=data,
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)
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r.raise_for_status()
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body = r.json()
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if not with_segments:
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return body["text"]
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raw_segs = body.get("segments") or []
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if raw_segs:
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return [
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{"start": s["start"], "end": s["end"], "text": s["text"].strip()}
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for s in raw_segs
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]
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return [{"start": 0.0, "end": 9999.0, "text": body["text"].strip()}]
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async def _transcribe_remote_whispercpp(
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self,
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audio_path: str,
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language: str,
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base_url: str,
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with_segments: bool,
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) -> Union[str, list[dict]]:
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async with httpx.AsyncClient(timeout=300) as client:
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with open(audio_path, "rb") as f:
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data = {"language": language}
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if with_segments:
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data["response_format"] = "verbose_json"
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r = await client.post(
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f"{base_url}/inference",
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files={"file": ("audio.wav", f, "audio/wav")},
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data=data,
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)
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r.raise_for_status()
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body = r.json()
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if not with_segments:
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return body.get("text", "").strip()
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raw_segs = body.get("segments") or []
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if raw_segs:
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return [
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{"start": s["start"], "end": s["end"], "text": s["text"].strip()}
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for s in raw_segs
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]
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return [{"start": 0.0, "end": 9999.0, "text": body.get("text", "").strip()}]
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async def _transcribe_local(
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self,
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audio_path: str,
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language: str,
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model_name: str,
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device: str,
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with_segments: bool,
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) -> Union[str, list[dict]]:
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loop = asyncio.get_running_loop()
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model = self._get_model(model_name, device)
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segments, _ = await loop.run_in_executor(
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None,
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lambda: model.transcribe(audio_path, language=language),
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)
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segments = list(segments)
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if not with_segments:
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return "".join(seg.text for seg in segments).strip()
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return [
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{"start": seg.start, "end": seg.end, "text": seg.text.strip()}
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for seg in segments
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if seg.text.strip()
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]
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engine = TranscriptionEngine()
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