Swapped back to qwen3-tts
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@@ -1,37 +1,33 @@
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FROM nvidia/cuda:12.4.1-devel-ubuntu22.04
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# Prevent interactive prompts
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ENV DEBIAN_FRONTEND=noninteractive
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ENV NVIDIA_VISIBLE_DEVICES=all
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ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
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# 1. Install Python 3.12 and SoX dependencies
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RUN apt-get update && apt-get install -y software-properties-common && \
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add-apt-repository ppa:deadsnakes/ppa -y && \
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apt-get update && apt-get install -y \
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python3.12 \
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python3.12-dev \
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curl \
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git \
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libsndfile1 \
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ffmpeg \
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sox \
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libsox-dev && \
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# No deadsnakes PPA needed. Native Python 3.10 works perfectly.
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RUN apt-get update && apt-get install -y \
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python3 python3-dev python3-pip curl git libsndfile1 ffmpeg sox libsox-dev ninja-build && \
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rm -rf /var/lib/apt/lists/*
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# 2. Use the official bootstrap to install a clean Pip for 3.12
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RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.12
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WORKDIR /app
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# 3. Explicitly install BOTH torch and torchaudio from the cu124 index
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RUN python3.12 -m pip install --no-cache-dir torch==2.6.0 torchaudio --index-url https://download.pytorch.org/whl/cu124
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RUN python3.12 -m pip install --no-cache-dir fastapi uvicorn numpy soundfile
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# Install Torch and core packages
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RUN python3 -m pip install --no-cache-dir torch==2.6.0 torchaudio --index-url https://download.pytorch.org/whl/cu124
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# 4. Install the local Qwen3-TTS requirements
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RUN python3.12 -m pip install --no-cache-dir faster-qwen3-tts
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# 1. Install foundational build tools and numpy first
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RUN python3 -m pip install --no-cache-dir numpy setuptools wheel ninja packaging psutil
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COPY tts-server.py .
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# 2. Install the rest of the stack that relies on numpy being present
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RUN python3 -m pip install --no-cache-dir fastapi uvicorn soundfile librosa transformers==4.57.3 accelerate sox onnxruntime
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# Force ABI compatibility for the C++ compiler
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ENV _GLIBCXX_USE_CXX11_ABI=0
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ENV MAX_JOBS=8
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# Install flash-attn
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RUN python3 -m pip install --no-cache-dir flash-attn --no-build-isolation
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COPY swarm-control/indra-tts-server/tts-server.py .
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EXPOSE 8002
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CMD ["python3.12", "tts-server.py"]
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CMD ["python3", "tts-server.py"]
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@@ -1,74 +1,98 @@
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import os
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import torch
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import numpy as np
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import io
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import wave
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import soundfile as sf
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from fastapi import FastAPI, HTTPException
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from fastapi.responses import Response
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from pydantic import BaseModel
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from faster_qwen3_tts import FasterQwen3TTS
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from qwen_tts import Qwen3TTSModel
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app = FastAPI(title="Indra tts")
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# --- 1. HARDWARE OPTIMIZATIONS ---
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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torch.set_float32_matmul_precision('high')
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if not torch.cuda.is_available():
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raise RuntimeError("Mouth cannot find CUDA. Check nvidia-container-toolkit.")
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print(f"Loading model on: {torch.cuda.get_device_name(0)}")
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# NEW: Confine PyTorch CPU threads. On massive servers, PyTorch tries to use
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# all available cores for background tasks, causing severe context-switching lag.
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torch.set_num_threads(4)
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# Load the Base model for high-fidelity mimicry
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model = FasterQwen3TTS.from_pretrained(
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app = FastAPI(title="Indra Mouth - Qwen3-TTS Official (Optimized)")
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prompt_cache = {}
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print(f"Loading Official Qwen3-TTS on: {torch.cuda.get_device_name(0)}")
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model = Qwen3TTSModel.from_pretrained(
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"Qwen/Qwen3-TTS-12Hz-1.7B-Base",
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device="cuda:0", # Targets GPU 7
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dtype=torch.bfloat16
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device_map="cuda:0",
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dtype=torch.bfloat16,
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attn_implementation="sdpa"
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)
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model.eval()
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# --- 2. THE SURGICAL COMPILE ---
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# We bypass the wrapper class and strictly compile the heavy LLM engine
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# --- 2. THE SURGICAL COMPILE ---
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import torch._dynamo
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import torch._inductor.config
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torch._dynamo.config.suppress_errors = True
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# Expand the cache limit so it doesn't thrash when sequence lengths vary
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torch._dynamo.config.cache_size_limit = 128
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# Force inductor to use the fastest possible memory layouts for L40S
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torch._inductor.config.coordinate_descent_tuning = True
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print("Compiling Autoregressive Engine (Dynamic Shapes)...")
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model.talker = torch.compile(
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model.talker,
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mode="max-autotune", # The most aggressive optimization level available
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dynamic=True # CRITICAL: Tells the compiler the audio length will grow
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)
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class TTSRequest(BaseModel):
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model: str = "tts-1" # ignored by backend, here to satisfy modelix router
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input: str
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voice: str = "oni"
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response_format: str = "wav"
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seed: int = 42
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@app.post("/v1/audio/speech")
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async def generate_speech(request: TTSRequest):
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try:
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voice_file = f"{request.voice}.wav"
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base_path = "/mnt/nvme3n1/swarm/voice-samples"
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ref_path = os.path.join(base_path, voice_file)
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txt_path = os.path.splitext(ref_path)[0] + ".txt"
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voice_key = request.voice
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if voice_key not in prompt_cache:
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base_path = "/mnt/nvme3n1/swarm/voice-samples"
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ref_path = os.path.join(base_path, f"{voice_key}.wav")
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txt_path = os.path.join(base_path, f"{voice_key}.txt")
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if not os.path.exists(ref_path):
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raise FileNotFoundError(f"Voice sample {ref_path} not found.")
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ref_text = None
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if os.path.exists(txt_path):
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with open(txt_path, "r") as f:
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ref_text = f.read().strip()
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ref_text = None
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if os.path.exists(txt_path):
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with open(txt_path, "r") as f:
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ref_text = f.read().strip()
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prompt_cache[voice_key] = model.create_voice_clone_prompt(
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ref_audio=ref_path,
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ref_text=ref_text,
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x_vector_only_mode=(ref_text is None)
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)
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print(f"--- Cached pristine voice prompt: {voice_key} ---")
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# Fix the seed for the persona identity
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torch.manual_seed(request.seed)
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full_audio = []
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# Non-streaming call is fine here since it takes <1s on your L40S
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audio_data, sample_rate = model.generate_voice_clone(
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text=request.input,
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language="English",
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ref_audio=ref_path,
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ref_text=ref_text,
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xvec_only=(ref_text is None)
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)
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audio_data = np.array(audio_data)
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audio_data = audio_data.flatten()
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# Convert Float32 to Int16 for standard WAV compatibility
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audio_int16 = (audio_data * 32767).astype(np.int16)
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with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16):
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wavs, sr = model.generate_voice_clone(
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text=[request.input],
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language=["English"],
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voice_clone_prompt=prompt_cache[voice_key]
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)
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wav_io = io.BytesIO()
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with wave.open(wav_io, 'wb') as wav_file:
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wav_file.setnchannels(1)
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wav_file.setsampwidth(2)
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wav_file.setframerate(sample_rate)
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wav_file.writeframes(audio_int16.tobytes())
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sf.write(wav_io, wavs[0], sr, format='WAV', subtype='FLOAT')
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wav_io.seek(0)
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return Response(content=wav_io.getvalue(), media_type="audio/wav")
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except Exception as e:
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