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[Model] Support MOSS-TTS-local-v1.5 (#4664)

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Signed-off-by: gcanlin <canlinguosdu@gmail.com>
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Canlin Guo authored and GitHub committed 2026-06-25 15:43:03 +08:00
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commit 2e6c83c89c
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examples/offline_inference/text_to_speech/moss_tts/end2end.py
+25 -12
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@@ -1,11 +1,12 @@
"""Offline inference example for MOSS-TTS variants.
Covers all five models:
- OpenMOSS-Team/MOSS-TTS (8B, general TTS)
- OpenMOSS-Team/MOSS-TTSD-v1.0 (8B, dialogue TTS)
- OpenMOSS-Team/MOSS-SoundEffect (8B, sound effect synthesis)
- OpenMOSS-Team/MOSS-TTS-Realtime (1.7B, low-latency streaming)
- OpenMOSS-Team/MOSS-VoiceGenerator(1.7B, voice design)
Covers six models:
- OpenMOSS-Team/MOSS-TTS (8B, general TTS)
- OpenMOSS-Team/MOSS-TTSD-v1.0 (8B, dialogue TTS)
- OpenMOSS-Team/MOSS-SoundEffect (8B, sound effect synthesis)
- OpenMOSS-Team/MOSS-TTS-Realtime (1.7B, low-latency streaming)
- OpenMOSS-Team/MOSS-VoiceGenerator (1.7B, voice design)
- OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5 (n_vq=12, 48 kHz stereo)
Usage
-----
@@ -210,12 +211,18 @@ def run_tts(args: argparse.Namespace) -> None:
"OpenMOSS-Team/MOSS-SoundEffect": "moss_sound_effect",
"OpenMOSS-Team/MOSS-TTS-Realtime": "moss_tts_realtime",
"OpenMOSS-Team/MOSS-VoiceGenerator": "moss_voice_generator",
"OpenMOSS-Team/MOSS-TTS-Local-Transformer-v1.5": "moss_tts_local",
}
deploy_config = deploy_map.get(args.model, "moss_tts")
is_sound_effect = "SoundEffect" in args.model
is_voice_gen = "VoiceGenerator" in args.model
is_realtime = "Realtime" in args.model
# Local-v1.5 ships its own AutoProcessor (processor_config.json +
# processing_moss_tts.py), so it reuses the same _build_unified_codes
# path as the Delay variants below — just a different n_vq/codec.
is_local = "Local" in args.model
output_sr = 48000 if is_local else 24000
ref_audio = None
builder_kwargs: dict = {}
@@ -314,27 +321,33 @@ def run_tts(args: argparse.Namespace) -> None:
wav = mm.get("model_outputs")
if wav is None:
continue
# Local-v1.5's codec emits stereo chunks shaped (C, T); keep the
# channel axis intact (don't flatten) so concatenation below stays
# along the time axis. Mono variants stay (T,) as before.
if isinstance(wav, list):
non_empty = [w.reshape(-1) for w in wav if isinstance(w, torch.Tensor) and w.numel() > 0]
non_empty = [w for w in wav if isinstance(w, torch.Tensor) and w.numel() > 0]
if non_empty and t_first_audio is None:
t_first_audio = _time.monotonic()
chunks.extend(non_empty)
elif isinstance(wav, torch.Tensor) and wav.numel() > 0:
if t_first_audio is None:
t_first_audio = _time.monotonic()
chunks.append(wav.reshape(-1))
chunks.append(wav)
t_gen_end = _time.monotonic()
if not chunks:
print("No audio in output — check model loading logs.")
return
wav_t = torch.cat(chunks).float().cpu()
sf.write(args.output, wav_t.numpy(), 24000)
print(f"Saved {len(wav_t) / 24000:.2f}s of audio to {args.output}")
cat_dim = -1 if chunks[0].dim() == 2 else 0
wav_t = torch.cat(chunks, dim=cat_dim).float().cpu()
n_samples = wav_t.shape[-1]
# soundfile wants (T,) mono or (T, C) multi-channel.
sf.write(args.output, (wav_t.transpose(0, 1) if wav_t.dim() == 2 else wav_t).numpy(), output_sr)
print(f"Saved {n_samples / output_sr:.2f}s of audio to {args.output}")
ttfa_ms = (t_first_audio - t_gen_start) * 1000.0 if t_first_audio is not None else float("nan")
gen_total_ms = (t_gen_end - t_gen_start) * 1000.0
print(f"TTFA_MS={ttfa_ms:.1f} GEN_TOTAL_MS={gen_total_ms:.1f} AUDIO_S={len(wav_t) / 24000:.2f}")
print(f"TTFA_MS={ttfa_ms:.1f} GEN_TOTAL_MS={gen_total_ms:.1f} AUDIO_S={n_samples / output_sr:.2f}")
def main() -> None: