init kokoro

bi_v100-kokoro/Dockerfile_kokoro

@@ -6,4 +6,7 @@ RUN pip install -r requirements_kokoro.txt -c constraints_kokoro.txt \
     && apt update \
     && apt install -y espeak-ng
 
+COPY ./en_core_web_sm-3.8.0.tar.gz .
+RUN pip install --no-index en_core_web_sm-3.8.0.tar.gz
+
 ENTRYPOINT ["/bin/bash", "launch_kokoro.sh"]

bi_v100-kokoro/constraints_kokoro.txt

@@ -1 +1,3 @@
-torch==2.1.0+corex.3.2.1
+torch==2.1.0+corex.3.2.1
+numpy==1.23.5
+scipy==1.14.1

bi_v100-kokoro/kokoro_server.py

@@ -1,19 +1,25 @@
 import os
+import io
 
-from fastapi import FastAPI, Body
+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
+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
+
 
 def conv_transpose1d_forward(self, input: Tensor, output_size: Optional[List[int]] = None) -> Tensor:
     if self.padding_mode != 'zeros':
@@ -35,16 +41,27 @@ torch.nn.ConvTranspose1d.forward = conv_transpose1d_forward
 
 
 repo_id = 'hexgrad/Kokoro-82M-v1.1-zh'
-# MODEL_SR = 24000
+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_pipeline = None
-zh_pipeline = None
-en_voice_pt = None
-zh_voice_pt = 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', '/models/hexgrad/Kokoro-82M-v1.1-zh')
+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':
@@ -63,29 +80,80 @@ def speed_callable(len_ps):
         speed = 1 - (len_ps - 83) / 500
     return speed
 
-# 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(audio: np.ndarray):
+# 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, en_pipeline, zh_pipeline
+    global model, en_empty_pipeline
-    global en_voice_pt, zh_voice_pt
+    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, 'kokoro-v1_1-zh.pth'), config=os.path.join(model_dir, 'config.json')).to(device).eval()
+    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):
@@ -95,29 +163,90 @@ async def lifespan(app: FastAPI):
 
 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}"
 
-# return 24kHz pcm-16
+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 generate(ssml: str = Body(...)):
+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:
-            text = voice_element.text.strip()
+            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():
-        if language == 'en':
+        texts = cut_sentences(transcription)
-            generator = en_pipeline(text=text, voice=en_voice_pt)
+        has_yield = False
-        else:
+        for text in texts:
-            generator = zh_pipeline(text=text, voice=zh_voice_pt, speed=speed_callable)
+            if text.strip() and contains_words(text):
-        for (_, _, audio) in generator:
+                pipeline = pipeline_dict[language].pipeline
-            yield audio_postprocess(audio.numpy()).tobytes()
+                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')
 
@@ -127,6 +256,19 @@ def generate(ssml: str = Body(...)):
 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)

bi_v100-kokoro/requirements_kokoro.txt

@@ -2,4 +2,4 @@ kokoro>=0.8.2
 misaki[zh]>=0.8.2
 soundfile
 fastapi
-uvicorn[standard]
+uvicorn[standard]