193 lines
7.2 KiB
Diff
193 lines
7.2 KiB
Diff
--- examples/openai_server.py 2026-06-26 11:11:13.425594803 +0200
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+++ /tmp/my_openai_server.py 2026-06-26 11:11:13.417691441 +0200
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@@ -72,6 +72,24 @@
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default_voice: Optional[str] = None
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SAMPLE_RATE = 24000 # updated once the model loads
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_model_lock = threading.Lock() # prevent concurrent GPU inference
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+aligner_model = None
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+
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+def _get_aligner():
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+ global aligner_model
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+ if aligner_model is None:
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+ try:
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+ from qwen_asr import Qwen3ForcedAligner
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+ import torch
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+ except ImportError:
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+ raise HTTPException(status_code=500, detail="qwen-asr is not installed. Run: pip install qwen-asr")
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+ logger.info("Loading Qwen3-ForcedAligner-0.6B...")
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+ aligner_model = Qwen3ForcedAligner.from_pretrained(
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+ "Qwen/Qwen3-ForcedAligner-0.6B",
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+ dtype=torch.bfloat16,
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+ device_map="cuda"
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+ )
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+ logger.info("Aligner loaded.")
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+ return aligner_model
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def _voice_seed(voice_name: str) -> int:
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@@ -99,8 +117,8 @@
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model: str = "tts-1"
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input: str
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voice: str = "alloy"
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- response_format: str = "wav" # wav | pcm | mp3
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- speed: float = 1.0 # accepted but not yet applied
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+ response_format: str = "wav" # wav | pcm | mp3 | zip
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+ speed: float = 1.0 # scales audio tempo
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# ---------------------------------------------------------------------------
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@@ -243,7 +261,7 @@
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return None
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-async def _stream_chunks(voice_cfg: dict, text: str, voice_name: str) -> AsyncGenerator[bytes, None]:
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+async def _stream_chunks(voice_cfg: dict, text: str, voice_name: str, speed: float) -> AsyncGenerator[bytes, None]:
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"""
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Run generate_voice_clone_streaming in a background thread and yield
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raw PCM bytes for each chunk as they arrive.
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@@ -252,6 +270,31 @@
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_DONE = object()
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def producer():
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+ process = None
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+ if speed != 1.0:
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+ import subprocess
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+ cmd = [
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+ "ffmpeg", "-y", "-loglevel", "error",
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+ "-f", "s16le", "-ar", str(SAMPLE_RATE), "-ac", "1", "-i", "pipe:0",
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+ "-filter:a", f"atempo={speed}",
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+ "-f", "s16le", "-ar", str(SAMPLE_RATE), "-ac", "1", "pipe:1"
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+ ]
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+ process = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
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+
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+ def ffmpeg_reader():
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+ try:
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+ while True:
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+ out = process.stdout.read(4096)
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+ if not out:
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+ break
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+ q.put(out)
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+ except Exception as e:
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+ q.put(e)
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+ finally:
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+ q.put(_DONE)
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+
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+ threading.Thread(target=ffmpeg_reader, daemon=True).start()
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+
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try:
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with _model_lock:
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_seed_rng(voice_cfg.get("seed", _voice_seed(voice_name)))
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@@ -268,11 +311,22 @@
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top_k=voice_cfg.get("top_k", 50),
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top_p=voice_cfg.get("top_p", 0.9),
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):
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- q.put(chunk)
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+ raw = _to_pcm16(chunk)
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+ if process:
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+ process.stdin.write(raw)
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+ process.stdin.flush()
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+ else:
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+ q.put(raw)
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except Exception as exc:
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q.put(exc)
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finally:
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- q.put(_DONE)
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+ if process:
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+ try:
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+ process.stdin.close()
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+ except Exception:
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+ pass
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+ else:
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+ q.put(_DONE)
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thread = threading.Thread(target=producer, daemon=True)
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thread.start()
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@@ -284,7 +338,7 @@
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break
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if isinstance(item, Exception):
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raise item
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- yield _to_pcm16(item)
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+ yield item
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# ---------------------------------------------------------------------------
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@@ -301,7 +355,8 @@
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async def create_speech(req: SpeechRequest):
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if tts_model is None:
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raise HTTPException(status_code=503, detail="Model not loaded")
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- if not req.input.strip():
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+ req.input = req.input.strip()
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+ if not req.input:
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raise HTTPException(status_code=400, detail="'input' text is empty")
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voice_cfg = resolve_voice(req.voice)
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@@ -311,16 +366,17 @@
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"wav": "audio/wav",
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"pcm": "audio/pcm",
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"mp3": "audio/mpeg",
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+ "zip": "application/zip",
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}
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if fmt not in _CONTENT_TYPES:
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raise HTTPException(
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status_code=400,
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- detail=f"response_format {fmt!r} not supported. Use: wav, pcm, mp3",
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+ detail=f"response_format {fmt!r} not supported. Use: wav, pcm, mp3, zip",
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)
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content_type = _CONTENT_TYPES[fmt]
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- # --- MP3: generate all audio, then encode (non-streaming) ---
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- if fmt == "mp3":
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+ # --- MP3 / ZIP: generate all audio, then encode (non-streaming) ---
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+ if fmt in ("mp3", "zip"):
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loop = asyncio.get_event_loop()
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def _generate():
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@@ -341,13 +397,46 @@
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audio_arrays, sr = await loop.run_in_executor(None, _generate)
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audio = audio_arrays[0] if audio_arrays else np.zeros(1, dtype=np.float32)
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+
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+ if req.speed != 1.0:
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+ import subprocess
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+ cmd = [
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+ "ffmpeg", "-y", "-loglevel", "error",
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+ "-f", "f32le", "-ar", str(sr), "-ac", "1", "-i", "pipe:0",
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+ "-filter:a", f"atempo={req.speed}",
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+ "-f", "f32le", "-ar", str(sr), "-ac", "1", "pipe:1"
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+ ]
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+ process = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE)
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+ process.stdin.write(audio.tobytes())
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+ process.stdin.close()
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+ out = process.stdout.read()
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+ audio = np.frombuffer(out, dtype=np.float32)
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+
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+ if fmt == "zip":
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+ def _align():
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+ aligner = _get_aligner()
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+ res = aligner.align(audio=(audio, sr), text=req.input, language=voice_cfg.get("language", "Auto"))
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+ import dataclasses
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+ return [dataclasses.asdict(x) for x in res]
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+
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+ align_data = await loop.run_in_executor(None, _align)
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+
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+ import zipfile
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+ mp3_bytes = _to_mp3_bytes(audio, sr)
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+ zip_buf = io.BytesIO()
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+ with zipfile.ZipFile(zip_buf, "w", zipfile.ZIP_DEFLATED) as zf:
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+ zf.writestr("audio.mp3", mp3_bytes)
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+ zf.writestr("timer.json", json.dumps(align_data, ensure_ascii=False))
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+
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+ return Response(content=zip_buf.getvalue(), media_type=content_type)
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+
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return Response(content=_to_mp3_bytes(audio, sr), media_type=content_type)
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# --- WAV / PCM: stream chunks as they are generated ---
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async def audio_stream():
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if fmt == "wav":
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yield _wav_header(SAMPLE_RATE) # stream with unknown data length
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- async for raw_chunk in _stream_chunks(voice_cfg, req.input, req.voice):
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+ async for raw_chunk in _stream_chunks(voice_cfg, req.input, req.voice, req.speed):
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yield raw_chunk
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return StreamingResponse(audio_stream(), media_type=content_type)
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