init muxi

metaX-C500-kokoro/kokoro_server.py

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+import os
+import io
+
+from fastapi import FastAPI, Response, Body, HTTPException
+from fastapi.responses import StreamingResponse, JSONResponse
+from contextlib import asynccontextmanager
+import uvicorn
+import xml.etree.ElementTree as ET
+
+from kokoro import KPipeline, KModel
+# import soundfile as sf
+import wave
+import numpy as np
+from scipy.signal import resample
+
+import torch
+from torch import Tensor
+from torch.nn import functional as F
+from typing import Optional, List
+import re
+from dataclasses import dataclass
+
+repo_id = 'hexgrad/Kokoro-82M-v1.1-zh'
+MODEL_SR = 24000
+TARGET_SR = 16000
+# How much silence to insert between paragraphs: 5000 is about 0.2 seconds
+N_ZEROS = 20
+model = None
+en_empty_pipeline = None
+en_voice = os.getenv('EN_VOICE', 'af_maple.pt')
+zh_voice = os.getenv('ZH_VOICE', 'zf_046.pt')
+model_dir = os.getenv('MODEL_DIR', '/model/hexgrad')
+model_name = os.getenv('MODEL_NAME','kokoro-v1_1-zh.pth')
+
+# model_1_1_dir = os.path.join(model_dir, 'Kokoro-82M-v1.1-zh')
+# model_1_0_dir = os.path.join(model_dir, 'Kokoro-82M')
+# repo_id_1_0 = 'hexgrad/Kokoro-82M'
+
+@dataclass
+class LanguagePipeline:
+    pipeline: KPipeline
+    voice_pt: str
+
+pipeline_dict: dict[str, LanguagePipeline] = {}
+
+def en_callable(text):
+    if text == 'Kokoro':
+        return 'kˈOkəɹO'
+    elif text == 'Sol':
+        return 'sˈOl'
+    return next(en_empty_pipeline(text)).phonemes
+
+# HACK: Mitigate rushing caused by lack of training data beyond ~100 tokens
+# Simple piecewise linear fn that decreases speed as len_ps increases
+def speed_callable(len_ps):
+    speed = 0.8
+    if len_ps <= 83:
+        speed = 1
+    elif len_ps < 183:
+        speed = 1 - (len_ps - 83) / 500
+    return speed
+
+
+# from https://huggingface.co/spaces/coqui/voice-chat-with-mistral/blob/main/app.py
+def wave_header_chunk(frame_input=b"", channels=1, sample_width=2, sample_rate=32000):
+    # This will create a wave header then append the frame input
+    # It should be first on a streaming wav file
+    # Other frames better should not have it (else you will hear some artifacts each chunk start)
+    wav_buf = io.BytesIO()
+    with wave.open(wav_buf, "wb") as vfout:
+        vfout.setnchannels(channels)
+        vfout.setsampwidth(sample_width)
+        vfout.setframerate(sample_rate)
+        vfout.writeframes(frame_input)
+
+    wav_buf.seek(0)
+    return wav_buf.read()
+
+
+def resample_audio(data: np.ndarray, original_rate: int, target_rate: int):
+    ori_dtype = data.dtype
+    # data = normalize_audio(data)
+    number_of_samples = int(len(data) * float(target_rate) / original_rate)
+    resampled_data = resample(data, number_of_samples)
+    # resampled_data = normalize_audio(resampled_data)
+    return resampled_data.astype(ori_dtype)
+
+def audio_postprocess(data: np.ndarray, original_rate: int, target_rate: int):
+    audio = resample_audio(data, original_rate, target_rate)
+    if audio.dtype == np.float32:
+        audio = np.int16(audio * 32767)
+        audio = np.concatenate([audio, np.zeros(N_ZEROS, dtype=np.int16)])
+    return audio
+
+
+# ================== decoder/istftnet 补丁 ==================
+
+def _to_cpu_fp32(obj):
+    if torch.is_tensor(obj):
+        return obj.detach().to("cpu", dtype=torch.float32)
+    if isinstance(obj, (list, tuple)):
+        return type(obj)(_to_cpu_fp32(x) for x in obj)
+    if isinstance(obj, dict):
+        return {k: _to_cpu_fp32(v) for k, v in obj.items()}
+    return obj
+
+def patch_decoder(model, device: str):
+    decoder = getattr(model, "decoder", None)
+    if decoder is None:
+        raise RuntimeError("未找到 model.decoder，请 print(model) 确认实际模块名。")
+
+    decoder.eval().to(device).float()
+    try: torch.nn.utils.remove_weight_norm(decoder)
+    except Exception: pass
+
+    for p in decoder.parameters():
+        p.requires_grad = False
+        if p.dtype != torch.float32: p.data = p.data.float()
+    for n, b in decoder.named_buffers():
+        if b.dtype != torch.float32: setattr(decoder, n, b.float())
+
+    orig_forward = decoder.forward
+
+    def forward_patched(*args, **kwargs):
+        # 关键：decoder 运行时，强制 FP32、并保证输入与 decoder 在同一设备
+        with torch.amp.autocast('cuda', enabled=False):
+            if device == "cpu":
+                args_ = _to_cpu_fp32(args); kwargs_ = _to_cpu_fp32(kwargs)
+                out = orig_forward(*args_, **kwargs_)
+                return _to_cpu_fp32(out)
+            else:  # device == "cuda"
+                def to_gpu_fp32(x):
+                    if torch.is_tensor(x):
+                        return x.detach().to("cuda", dtype=torch.float32)
+                    if isinstance(x, (list, tuple)):
+                        return type(x)(to_gpu_fp32(t) for t in x)
+                    if isinstance(x, dict):
+                        return {k: to_gpu_fp32(v) for k, v in x.items()}
+                    return x
+                args_ = to_gpu_fp32(args); kwargs_ = to_gpu_fp32(kwargs)
+                out = orig_forward(*args_, **kwargs_)
+                if torch.is_tensor(out):
+                    return out.detach().to(device, dtype=torch.float32)
+                return out
+
+    decoder.forward = forward_patched
+
+
+
+def init():
+    global model, en_empty_pipeline
+    global model_1_0
+    global pipeline_dict
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    model = KModel(repo_id=repo_id, model=os.path.join(model_dir, model_name), config=os.path.join(model_dir, 'config.json')).to(device).eval()
+    patch_decoder(model, "cpu")
+
+    en_empty_pipeline = KPipeline(lang_code='a', repo_id=repo_id, model=False)
+    en_pipeline = KPipeline(lang_code='a', repo_id=repo_id, model=model)
+    zh_pipeline = KPipeline(lang_code='z', repo_id=repo_id, model=model, en_callable=en_callable)
+    en_voice_pt = os.path.join(model_dir, 'voices', en_voice)
+    zh_voice_pt = os.path.join(model_dir, 'voices', zh_voice)
+    pipeline_dict['zh'] = LanguagePipeline(pipeline=zh_pipeline, voice_pt=zh_voice_pt)
+    pipeline_dict['en'] = LanguagePipeline(pipeline=en_pipeline, voice_pt=en_voice_pt)
+
+    # v1.0 model for other languages
+    # model_1_0 = KModel(repo_id=repo_id_1_0, model=os.path.join(model_1_0_dir, 'kokoro-v1_0.pth'), config=os.path.join(model_1_0_dir, 'config.json')).to(device).eval()
+    # # es
+    # es_pipeline = KPipeline(lang_code='e', repo_id=repo_id_1_0, model=model_1_0)
+    # es_voice_pt = os.path.join(model_1_0_dir, 'voices', 'ef_dora.pt')
+    # pipeline_dict['es'] = LanguagePipeline(pipeline=es_pipeline, voice_pt=es_voice_pt)
+    # # fr
+    # fr_pipeline = KPipeline(lang_code='f', repo_id=repo_id_1_0, model=model_1_0)
+    # fr_voice_pt = os.path.join(model_1_0_dir, 'voices', 'ff_siwis.pt')
+    # pipeline_dict['fr'] = LanguagePipeline(pipeline=fr_pipeline, voice_pt=fr_voice_pt)
+    # # hi
+    # hi_pipeline = KPipeline(lang_code='h', repo_id=repo_id_1_0, model=model_1_0)
+    # hi_voice_pt = os.path.join(model_1_0_dir, 'voices', 'hf_alpha.pt')
+    # pipeline_dict['hi'] = LanguagePipeline(pipeline=hi_pipeline, voice_pt=hi_voice_pt)
+    # # it
+    # it_pipeline = KPipeline(lang_code='i', repo_id=repo_id_1_0, model=model_1_0)
+    # it_voice_pt = os.path.join(model_1_0_dir, 'voices', 'if_sara.pt')
+    # pipeline_dict['it'] = LanguagePipeline(pipeline=it_pipeline, voice_pt=it_voice_pt)
+    # # ja
+    # ja_pipeline = KPipeline(lang_code='j', repo_id=repo_id_1_0, model=model_1_0)
+    # ja_voice_pt = os.path.join(model_1_0_dir, 'voices', 'jf_alpha.pt')
+    # pipeline_dict['ja'] = LanguagePipeline(pipeline=ja_pipeline, voice_pt=ja_voice_pt)
+    # # pt
+    # pt_pipeline = KPipeline(lang_code='p', repo_id=repo_id_1_0, model=model_1_0)
+    # pt_voice_pt = os.path.join(model_1_0_dir, 'voices', 'pf_dora.pt')
+    # pipeline_dict['pt'] = LanguagePipeline(pipeline=pt_pipeline, voice_pt=pt_voice_pt)
+
+    warmup()
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    init()
+    yield
+    pass
+
+app = FastAPI(lifespan=lifespan)
+
+def warmup():
+    zh_pipeline = pipeline_dict['zh'].pipeline
+    voice = pipeline_dict['zh'].voice_pt
+    generator = zh_pipeline(text="语音合成测试TTS。", voice=voice, speed=speed_callable)
+    for _ in generator:
+        pass
+
+xml_namespace = "{http://www.w3.org/XML/1998/namespace}"
+
+symbols = ',.!?;:()[]{}<>，。！？；：【】《》……"“”_—'
+def contains_words(text):
+    return any(char not in symbols for char in text)
+
+def cut_sentences(text) -> list[str]:
+    text = text.strip()
+    splits = re.split(r"([.;?!、。？！；])", text)
+    sentences = []
+    for i in range(0, len(splits), 2):
+        if i + 1 < len(splits):
+            s = splits[i] + splits[i + 1]
+        else:
+            s = splits[i]
+        s = s.strip()
+        if s:
+            sentences.append(s)
+    return sentences
+
+LANGUAGE_ALIASES = {
+    'z': 'zh',
+    'a': 'en',
+    'e': 'es',
+    'f': 'fr',
+    'h': 'hi',
+    'i': 'it',
+    'j': 'ja',
+    'p': 'pt',
+}
+
+@app.post("/")
+@app.post("/tts")
+def predict(ssml: str = Body(...), include_header: bool = False):
+    try:
+        root = ET.fromstring(ssml)
+        voice_element = root.find(".//voice")
+        if voice_element is not None:
+            transcription = voice_element.text.strip()
+            language = voice_element.get(f'{xml_namespace}lang', "zh").strip()
+            # voice_name = voice_element.get("name", "zh-f-soft-1").strip()
+        else:
+            return JSONResponse(status_code=400, content={"message": "Invalid SSML format: <voice> element not found."})
+    except ET.ParseError as e:
+        return JSONResponse(status_code=400, content={"message": "Invalid SSML format", "Exception": str(e)})
+    
+    if not contains_words(transcription):
+        audio = np.zeros(N_ZEROS, dtype=np.int16).tobytes()
+        if include_header:
+            audio_header = wave_header_chunk(sample_rate=TARGET_SR)
+            audio = audio_header + audio
+        return Response(audio, media_type='audio/wav')
+    
+    if language not in pipeline_dict:
+        if language in LANGUAGE_ALIASES:
+            language = LANGUAGE_ALIASES[language]
+        else:
+            return JSONResponse(status_code=400, content={"message": f"Language '{language}' not supported."})
+
+
+    def streaming_generator():
+        texts = cut_sentences(transcription)
+        has_yield = False
+        for text in texts:
+            if text.strip() and contains_words(text):
+                pipeline = pipeline_dict[language].pipeline
+                voice = pipeline_dict[language].voice_pt
+                if language == 'zh':
+                    generator = pipeline(text=text, voice=voice, speed=speed_callable)
+                else:
+                    generator = pipeline(text=text, voice=voice)
+
+                for (_, _, audio) in generator:
+                    if include_header and not has_yield:
+                        has_yield = True
+                        yield wave_header_chunk(sample_rate=TARGET_SR)
+                    yield audio_postprocess(audio.numpy(), MODEL_SR, TARGET_SR).tobytes()
+
+    return StreamingResponse(streaming_generator(), media_type='audio/wav')
+
+
+@app.get("/health")
+@app.get("/ready")
+async def ready():
+    return JSONResponse(status_code=200, content={"status": "ok"})
+
+@app.get("/health_check")
+async def health_check():
+    try:
+        a = torch.ones(10, 20, dtype=torch.float32, device='cuda')
+        b = torch.ones(20, 10, dtype=torch.float32, device='cuda')
+        c = torch.matmul(a, b)
+        if c.sum() == 10 * 20 * 10:
+            return {"status": "ok"}
+        else:
+            raise HTTPException(status_code=503)
+    except Exception as e:
+        print(f'health_check failed')
+        raise HTTPException(status_code=503)
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=80)