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feat: fully automate speaker embedding generation (v6.4)

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mARTin-B78 2026-06-20 20:52:53 +02:00
parent 37274e52ee
commit 4d7b2c5e0c
2 changed files with 86 additions and 14 deletions
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9
README.md
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@ -138,6 +138,7 @@ python config/auto_transcribe.py --api-url http://localhost:8010/v1/audio/transc
`config/generate_voices.py` runs on container startup and creates `config/voices.json` from your speaker files.
When you start using a new voice for the first time, the server will automatically do the heavy lifting to extract the voice's acoustic fingerprint (a "speaker embedding") and save it as a `.pt` file in the `config/speakers/` directory. Future requests will instantly load this `.pt` file instead of re-analyzing the audio, which dramatically speeds up Time To First Audio (TTFA).
## VoiceDesign voices
VoiceDesign does not need reference audio. Define reusable voice personalities in `config/voicedesign_voices.json`:
@ -340,6 +341,14 @@ The first request after container startup can be slower because CUDA graph captu
## Changelog
### v6.4 — 2026-06-20
**Feature: Fully Automated Speaker Embeddings (.pt files)**
- Integrated speaker embedding extraction directly into the API server (`openai_server.py`).
- When a new voice is requested for the first time, the server will automatically compute the speaker embedding and save it as a `.pt` file in `/config/speakers/`.
- Future requests for the same voice automatically use the `.pt` file instead of recalculating the prompt from the `.wav` or `.mp3` reference audio.
- This provides the massive TTFA speedup of precomputed embeddings without requiring any manual scripting or configuration.
### v6.3 — 2026-06-20
**Feature: Precomputed Speaker Embeddings (.pt files)**

91
patches/openai_server.patch
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@ -1,25 +1,63 @@
diff --git a/examples/openai_server.py b/examples/openai_server.py
index 61047ea..2f95bbd 100644
--- a/examples/openai_server.py
+++ b/examples/openai_server.py
@@ -169,6 +169,16 @@ def resolve_voice(voice_name: str) -> dict:
--- /tmp/upstream_openai_server.py 2026-06-20 20:43:53.380341702 +0200
+++ build/examples/openai_server.py 2026-06-20 20:52:17.725652965 +0200
@@ -145,6 +145,7 @@
def resolve_voice(voice_name: str) -> dict:
"""Return voice config dict or fall back to default, else raise 400."""
+ voice_name = voice_name.strip()
if voice_name in voices:
return voices[voice_name]
if default_voice and default_voice in voices:
@@ -168,7 +169,47 @@
# ---------------------------------------------------------------------------
+def _load_voice_clone_prompt(voice_cfg: dict):
-async def _stream_chunks(voice_cfg: dict, text: str) -> AsyncGenerator[bytes, None]:
+def _load_voice_clone_prompt(voice_cfg: dict, voice_name: str, tts_model):
+ spk_emb_path = voice_cfg.get("speaker_embeddings") or voice_cfg.get("speaker embeddings")
+ if spk_emb_path and os.path.isfile(spk_emb_path):
+ try:
+ return torch.load(spk_emb_path, map_location="cpu", weights_only=False)
+ except Exception as e:
+ logger.error("Failed to load speaker embeddings from %s: %s", spk_emb_path, e)
+ return None
+
+ # Auto-generate if missing
+ ref_audio = voice_cfg.get("ref_audio")
+ if not ref_audio or not os.path.isfile(ref_audio):
+ return None
+
+ logger.info("Precomputing and saving speaker embedding for voice %r...", voice_name)
+ try:
+ ref_text = voice_cfg.get("ref_text", "")
+ # generate prompt using the model's built-in helper
+ prompt_items = tts_model.model.create_voice_clone_prompt(ref_audio, [ref_text])
+ vcp = tts_model.model._prompt_items_to_voice_clone_prompt(prompt_items)
+
+ # save it to the speakers directory
+ # If the file path is already in voice_cfg but doesn't exist, use that, otherwise generate a path
+ if spk_emb_path and not os.path.exists(spk_emb_path) and spk_emb_path.endswith('.pt'):
+ pt_path = spk_emb_path
+ else:
+ pt_path = f"/config/speakers/{voice_name}.pt"
+
+ os.makedirs(os.path.dirname(pt_path), exist_ok=True)
+ torch.save(vcp, pt_path)
+ logger.info("Saved speaker embedding to %s", pt_path)
+
+ # update in memory so future requests skip generating
+ voice_cfg["speaker_embeddings"] = pt_path
+
+ return vcp
+ except Exception as e:
+ logger.error("Failed to precompute speaker embedding: %s", e)
+ return None
+
+
async def _stream_chunks(voice_cfg: dict, text: str) -> AsyncGenerator[bytes, None]:
+async def _stream_chunks(voice_cfg: dict, text: str, voice_name: str) -> AsyncGenerator[bytes, None]:
"""
Run generate_voice_clone_streaming in a background thread and yield
@@ -183,11 +193,15 @@ async def _stream_chunks(voice_cfg: dict, text: str) -> AsyncGenerator[bytes, No
raw PCM bytes for each chunk as they arrive.
@@ -182,10 +223,15 @@
for chunk, _sr, _timing in tts_model.generate_voice_clone_streaming(
text=text,
language=voice_cfg.get("language", "Auto"),
@ -27,9 +65,9 @@ index 61047ea..2f95bbd 100644
+ ref_audio=voice_cfg.get("ref_audio"),
ref_text=voice_cfg.get("ref_text", ""),
chunk_size=voice_cfg.get("chunk_size", 12),
instruct=voice_cfg.get("instruct"),
- non_streaming_mode=False,
+ voice_clone_prompt=_load_voice_clone_prompt(voice_cfg),
+ instruct=voice_cfg.get("instruct"),
+ voice_clone_prompt=_load_voice_clone_prompt(voice_cfg, voice_name, tts_model),
+ non_streaming_mode=True,
+ temperature=voice_cfg.get("temperature", 0.8),
+ top_k=voice_cfg.get("top_k", 50),
@ -37,15 +75,15 @@ index 61047ea..2f95bbd 100644
):
q.put(chunk)
except Exception as exc:
@@ -249,9 +263,14 @@ async def create_speech(req: SpeechRequest):
@@ -247,8 +293,14 @@
return tts_model.generate_voice_clone(
text=req.input,
language=voice_cfg.get("language", "Auto"),
- ref_audio=voice_cfg["ref_audio"],
+ ref_audio=voice_cfg.get("ref_audio"),
ref_text=voice_cfg.get("ref_text", ""),
instruct=voice_cfg.get("instruct"),
+ voice_clone_prompt=_load_voice_clone_prompt(voice_cfg),
+ instruct=voice_cfg.get("instruct"),
+ voice_clone_prompt=_load_voice_clone_prompt(voice_cfg, req.voice, tts_model),
+ non_streaming_mode=True,
+ temperature=voice_cfg.get("temperature", 0.8),
+ top_k=voice_cfg.get("top_k", 50),
@ -53,3 +91,28 @@ index 61047ea..2f95bbd 100644
)
audio_arrays, sr = await loop.run_in_executor(None, _generate)
@@ -259,7 +311,7 @@
async def audio_stream():
if fmt == "wav":
yield _wav_header(SAMPLE_RATE) # stream with unknown data length
- async for raw_chunk in _stream_chunks(voice_cfg, req.input):
+ async for raw_chunk in _stream_chunks(voice_cfg, req.input, req.voice):
yield raw_chunk
return StreamingResponse(audio_stream(), media_type=content_type)
@@ -306,6 +358,7 @@
p.add_argument("--host", default="0.0.0.0", help="Bind host (default: 0.0.0.0)")
p.add_argument("--port", type=int, default=8000, help="Bind port (default: 8000)")
p.add_argument("--device", default="cuda", help="Torch device (default: cuda)")
+ p.add_argument("--max-seq-len", type=int, default=4096, help="Max sequence length for CUDA graph static cache (default: 4096)")
return p.parse_args()
@@ -344,6 +397,7 @@
args.model,
device=args.device,
dtype=torch.bfloat16,
+ max_seq_len=args.max_seq_len,
)
SAMPLE_RATE = tts_model.sample_rate
logger.info("Model ready. Sample rate: %d Hz", SAMPLE_RATE)