update README

mlu_370-kokoro/Dockerfile_kokoro

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+FROM git.modelhub.org.cn:9443/enginex-cambricon/mlu370-pytorch:v25.01-torch2.5.0-torchmlu1.24.1-ubuntu22.04-py310
+
+WORKDIR /workspace
+
+ENV PATH=/torch/venv3/pytorch_infer/bin:/workspace/ffmpeg-mlu-v4.2.0/install/bin:/usr/local/neuware/bin:/usr/local/openmpi/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin
+RUN sed -i 's|source /torch/venv3/pytorch/bin/activate|source /torch/venv3/pytorch_infer/bin/activate|' /root/.bashrc
+
+RUN apt-get update && \
+    apt-get install -y espeak-ng && \
+    rm -rf /var/lib/apt/lists/*
+
+COPY requirements_kokoro.txt constraints_kokoro.txt kokoro_server.py en_core_web_sm-3.8.0.tar.gz /workspace/
+RUN pip install -r requirements_kokoro.txt 
+RUN pip install en_core_web_sm-3.8.0.tar.gz
+
+COPY launch_kokoro.sh /workspace/
+ENTRYPOINT ["/bin/bash", "launch_kokoro.sh"]

mlu_370-kokoro/README.md

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+# Kokoro-TTS
+
+本项目基于 **Kokoro** 模型封装，提供简洁的 Docker 部署方式，支持 **SSML 输入**，输出 **PCM 原始音频**，可用于语音合成。
+
+---
+
+## Quickstart
+
+### 1. 安装镜像
+```bash
+docker build -t tts:kokoro . -f Dockerfile_kokoro
+```
+
+### 2. 启动服务
+```bash
+docker run -it --rm \
+  -v /models/Kokoro-82M-v1.1-zh:/mnt/models \
+  --device=/dev/cambricon_dev0:/dev/cambricon_dev0 \
+  --device=/dev/cambricon_ctl:/dev/cambricon_ctl \
+  -p 8080:80 \
+  -e MODEL_DIR=/mnt/models \
+  -e MODEL_NAME=kokoro-v1_1-zh.pth \
+  tts:kokoro
+```
+
+参数说明：
+- `MODEL_DIR`：模型所在目录（挂载到容器内 `/mnt/models`）
+- `MODEL_NAME`：加载的模型文件名（通常为 `.safetensors`）
+- `-p 8080:80`：将容器内服务端口映射到宿主机 `8080`
+
+### 3. 测试服务
+```bash
+curl --request POST "http://localhost:8080/tts" \
+  --header 'Content-Type: application/ssml+xml' \
+  --header 'User-Agent: curl' \
+  --data-raw '<speak version="1.0" xml:lang="zh">
+    <voice xml:lang="zh" xml:gender="Female" name="zh">
+      今天天气很好，不知道明天天气怎么样。
+    </voice>
+  </speak>' \
+  --output sound.pcm
+```
+
+---

mlu_370-kokoro/constraints_kokoro.txt

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+torch==2.1.0+corex.3.2.1
+numpy==1.23.5
+scipy==1.14.1

mlu_370-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
+
+import kokoro.istftnet as _ist
+
+def _inverse_no_complex(self, magnitude, phase):
+    """
+    解决 MLU 设备上不支持复数计算
+    """
+    device = magnitude.device
+    dtype = magnitude.dtype
+
+    win_dev = torch.hann_window(self.win_length, device=device, dtype=dtype)
+
+    real = magnitude * torch.cos(phase)
+    imag = magnitude * torch.sin(phase)
+    spec_ri = torch.stack([real, imag], dim=-1).contiguous()  # (..., 2)
+
+    real_cpu = real.to("cpu")
+    imag_cpu = imag.to("cpu")
+    spec_complex_cpu = torch.complex(real_cpu, imag_cpu)  # (..,) 复数张量
+    win_cpu = torch.hann_window(self.win_length, device="cpu", dtype=dtype)
+
+    wav_cpu = torch.istft(
+        spec_complex_cpu,
+        n_fft=self.filter_length,
+        hop_length=self.hop_length,
+        win_length=self.win_length,
+        window=win_cpu,
+        center=True,
+        normalized=False,
+        onesided=True,
+    )
+    return wav_cpu.to(device).unsqueeze(-2)
+
+def _transform_no_complex(self, input_data):
+    """
+    纯实数 STFT：return_complex=False，随后手动求幅度与相位
+    """
+    z = torch.stft(
+        input_data,
+        n_fft=self.filter_length,
+        hop_length=self.hop_length,
+        win_length=self.win_length,
+        window=self.window.to(input_data.device, dtype=input_data.dtype),
+        return_complex=False,
+        center=True,
+        normalized=False,
+    )
+    real = z[..., 0]
+    imag = z[..., 1]
+    magnitude = torch.sqrt(real * real + imag * imag)
+    phase = torch.atan2(imag, real)
+    return magnitude, phase
+
+# 替换 Kokoro 的 STFT.inverse 实现
+_ist.TorchSTFT.inverse = _inverse_no_complex
+_ist.TorchSTFT.transform = _transform_no_complex
+
+
+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
+
+def init():
+    global model, en_empty_pipeline
+    global model_1_0
+    global pipeline_dict
+    # device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    device = 'mlu'
+    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()
+    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)

mlu_370-kokoro/launch_kokoro.sh

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+#!/bin/bash
+
+python3 kokoro_server.py
+

mlu_370-kokoro/requirements_kokoro.txt

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+kokoro>=0.8.2
+misaki[zh]>=0.8.2
+soundfile
+fastapi
+uvicorn[standard]