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https://github.com/Jeuners/astra-vision.git
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991f0d1eb5
Stimmen werden jetzt lazy pro Name geladen und gecacht statt einer fest verdrahteten Default-Stimme; das UI bekommt ein Dropdown mit allen 26 deutschen Pocket-TTS-Stimmen (/api/voices), Auswahl wird im Browser gemerkt und ist während eines laufenden Gesprächs gesperrt. Zusätzlich ein optionaler ASTRA_TAILNET_HOST, damit die App über `tailscale serve` auch von einem anderen Gerät im selben Tailnet erreichbar ist, ohne die Loopback-only-Härtung für alle anderen Hosts aufzuweichen. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LVgSHNHdRx3UNTBodFmhRA
116 lines
4.7 KiB
Python
116 lines
4.7 KiB
Python
"""Thread-confined model inference. No audio is stored on disk."""
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import asyncio
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from concurrent.futures import ThreadPoolExecutor
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import numpy as np
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from astra.core import Settings
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async def on_executor(executor, function, *args):
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"""Finish an in-flight native call before allowing its owner to be reused."""
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future = asyncio.get_running_loop().run_in_executor(executor, function, *args)
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try:
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return await asyncio.shield(future)
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except asyncio.CancelledError:
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await asyncio.shield(future)
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raise
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def drain_stream(stream):
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"""Let Pocket TTS join its internal threads before reusing the model.
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Its upstream generator only joins on normal exhaustion, not generator.close().
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Discard the unplayed remainder while the audio transport stops immediately.
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"""
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for _ in stream:
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pass
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class Models:
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def __init__(self, settings: Settings):
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self.settings = settings
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self.stt_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="astra-stt")
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self.tts_executor = ThreadPoolExecutor(max_workers=1, thread_name_prefix="astra-tts")
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self.stt = None
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self.tts = None
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self.voice_cache: dict[str, dict] = {}
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def load_stt(self):
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from mlx_audio.stt import load
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self.stt = load(self.settings.stt_model)
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# Ensure this installed MLX version really exposes incremental microphone APIs.
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from mlx_audio.stt.models.nemotron_asr.audio import StreamingLogMelSpectrogram # noqa: F401
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from mlx_audio.stt.models.nemotron_asr.streaming import (
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ConformerStreamingState, # noqa: F401
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)
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def load_tts(self):
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from pocket_tts import TTSModel
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self.tts = TTSModel.load_model(language=self.settings.tts_language)
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def get_voice(self, name: str) -> dict:
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"""Compute (or reuse) one voice's conditioning state. Runs on tts_executor."""
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if name not in self.voice_cache:
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self.voice_cache[name] = self.tts.get_state_for_audio_prompt(name)
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return self.voice_cache[name]
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async def close(self):
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await asyncio.to_thread(self.stt_executor.shutdown, wait=True, cancel_futures=True)
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await asyncio.to_thread(self.tts_executor.shutdown, wait=True, cancel_futures=True)
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class Recognizer:
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"""One utterance's incremental mel, encoder and RNN-T decoder state.
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All methods run on Models.stt_executor, including construction. Encoder and
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decoder caches survive microphone chunks and are discarded at utterance end.
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"""
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def __init__(self, model):
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from mlx_audio.stt.models.nemotron_asr.audio import StreamingLogMelSpectrogram
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from mlx_audio.stt.models.nemotron_asr.streaming import ConformerStreamingState
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self.model = model
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self.mel = StreamingLogMelSpectrogram(model.preprocessor_config)
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self.encoder = ConformerStreamingState(model.encoder, att_context_size=[56, 3])
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self.last_token = model.blank_id
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self.hidden = None
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self.tokens = []
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self.closed = False
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def push(self, pcm: bytes, final: bool = False) -> str:
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import mlx.core as mx
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from mlx_audio.stt.models.nemotron_asr import tokenizer
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if self.closed:
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raise RuntimeError("Utterance already closed")
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samples = mx.array(np.frombuffer(pcm, dtype="<i2").astype(np.float32) / 32768.0)
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mel = self.mel.push(samples, final=final)
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for encoded in self.encoder.push(mel, final=final):
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prompted = self.model.apply_prompt(encoded, "de-DE")
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self.encoder.materialize(prompted)
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for frame_index in range(prompted.shape[1]):
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feature = prompted[:, frame_index : frame_index + 1]
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for _ in range(self.model.max_symbols or 10):
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token = (
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mx.array([[self.last_token]], dtype=mx.int32)
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if self.last_token != self.model.blank_id
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else None
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)
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output, (h, c) = self.model.decoder(token, self.hidden)
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prediction = int(
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mx.argmax(self.model.joint(feature, output.astype(feature.dtype)))
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)
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if prediction == self.model.blank_id:
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break
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self.last_token = prediction
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self.hidden = (h.astype(feature.dtype), c.astype(feature.dtype))
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mx.eval(*self.hidden)
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if not tokenizer.is_special_token(prediction, self.model.vocabulary):
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self.tokens.append(prediction)
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self.closed = final
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return tokenizer.decode(self.tokens, self.model.vocabulary).strip()
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