update webui & fastapi for cosy2.0

runtime/python/fastapi/client.py

@@ -40,13 +40,23 @@ def main():
         }
         files = [('prompt_wav', ('prompt_wav', open(args.prompt_wav, 'rb'), 'application/octet-stream'))]
         response = requests.request("GET", url, data=payload, files=files, stream=True)
-    else:
+    elif args.mode == 'instruct':
         payload = {
             'tts_text': args.tts_text,
             'spk_id': args.spk_id,
             'instruct_text': args.instruct_text
         }
         response = requests.request("GET", url, data=payload, stream=True)
+    else:
+        # instruct2
+        url = url + "_v2"
+        payload = {
+            'tts_text': args.tts_text,
+            'instruct_text': args.instruct_text,
+            'format': 'pcm'  # option
+        }
+        files = [('prompt_wav', ('prompt_wav', open(args.prompt_wav, 'rb'), 'application/octet-stream'))]
+        response = requests.request("GET", url, data=payload, files=files, stream=True)
     tts_audio = b''
     for r in response.iter_content(chunk_size=16000):
         tts_audio += r
@@ -66,7 +76,7 @@ def main():
                         default='50000')
     parser.add_argument('--mode',
                         default='sft',
-                        choices=['sft', 'zero_shot', 'cross_lingual', 'instruct'],
+                        choices=['sft', 'zero_shot', 'cross_lingual', 'instruct', 'instruct2'],
                         help='request mode')
     parser.add_argument('--tts_text',
                         type=str,

runtime/python/fastapi/server.py

@@ -13,20 +13,27 @@
 # limitations under the License.
 import os
 import sys
+import io
 import argparse
 import logging
 logging.getLogger('matplotlib').setLevel(logging.WARNING)
 from fastapi import FastAPI, UploadFile, Form, File
-from fastapi.responses import StreamingResponse
+from fastapi.responses import StreamingResponse, Response
 from fastapi.middleware.cors import CORSMiddleware
 import uvicorn
 import numpy as np
-ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
-sys.path.append('{}/../../..'.format(ROOT_DIR))
-sys.path.append('{}/../../../third_party/Matcha-TTS'.format(ROOT_DIR))
-from cosyvoice.cli.cosyvoice import CosyVoice
+import torch
+import torchaudio
+CURR_DIR = os.path.dirname(os.path.abspath(__file__))
+ROOT_DIR = f'{CURR_DIR}/../../..'
+sys.path.append(f'{ROOT_DIR}')
+sys.path.append(f'{ROOT_DIR}/third_party/Matcha-TTS')
+from cosyvoice.cli.cosyvoice import CosyVoice, CosyVoice2
 from cosyvoice.utils.file_utils import load_wav
 
+model_dir = f"{ROOT_DIR}/pretrained_models/CosyVoice2-0.5B"
+cosyvoice = CosyVoice2(model_dir) if 'CosyVoice2' in model_dir else CosyVoice(model_dir)
+
 app = FastAPI()
 # set cross region allowance
 app.add_middleware(
@@ -37,47 +44,78 @@
     allow_headers=["*"])
 
 
+# 非流式wav数据
+def build_data(model_output):
+    tts_speeches = []
+    for i in model_output:
+        tts_speeches.append(i['tts_speech'])
+    output = torch.concat(tts_speeches, dim=1)
+
+    buffer = io.BytesIO()
+    torchaudio.save(buffer, output, 22050, format="wav")
+    buffer.seek(0)
+    return buffer.read(-1)
+
+
+# 流式pcm数据
 def generate_data(model_output):
     for i in model_output:
         tts_audio = (i['tts_speech'].numpy() * (2 ** 15)).astype(np.int16).tobytes()
         yield tts_audio
 
 
 @app.get("/inference_sft")
-async def inference_sft(tts_text: str = Form(), spk_id: str = Form()):
-    model_output = cosyvoice.inference_sft(tts_text, spk_id)
-    return StreamingResponse(generate_data(model_output))
+async def inference_sft(tts_text: str = Form(), spk_id: str = Form(), stream: bool = Form(default=False), format: str = Form(default="pcm")):
+    model_output = cosyvoice.inference_sft(tts_text, spk_id, stream=stream)
+    if format == "pcm":
+        return StreamingResponse(generate_data(model_output))
+    else:
+        return Response(build_data(model_output), media_type="audio/wav")
 
 
 @app.get("/inference_zero_shot")
-async def inference_zero_shot(tts_text: str = Form(), prompt_text: str = Form(), prompt_wav: UploadFile = File()):
+async def inference_zero_shot(tts_text: str = Form(), prompt_text: str = Form(), prompt_wav: UploadFile = File(), stream: bool = Form(default=False), format: str = Form(default="pcm")):
     prompt_speech_16k = load_wav(prompt_wav.file, 16000)
-    model_output = cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k)
-    return StreamingResponse(generate_data(model_output))
+    model_output = cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k, stream=stream)
+    if format == "pcm":
+        return StreamingResponse(generate_data(model_output))
+    else:
+        return Response(build_data(model_output), media_type="audio/wav")
 
 
 @app.get("/inference_cross_lingual")
-async def inference_cross_lingual(tts_text: str = Form(), prompt_wav: UploadFile = File()):
+async def inference_cross_lingual(tts_text: str = Form(), prompt_wav: UploadFile = File(), stream: bool = Form(default=False), format: str = Form(default="pcm")):
     prompt_speech_16k = load_wav(prompt_wav.file, 16000)
-    model_output = cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k)
-    return StreamingResponse(generate_data(model_output))
+    model_output = cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k, stream=stream)
+    if format == "pcm":
+        return StreamingResponse(generate_data(model_output))
+    else:
+        return Response(build_data(model_output), media_type="audio/wav")
 
 
 @app.get("/inference_instruct")
-async def inference_instruct(tts_text: str = Form(), spk_id: str = Form(), instruct_text: str = Form()):
-    model_output = cosyvoice.inference_instruct(tts_text, spk_id, instruct_text)
-    return StreamingResponse(generate_data(model_output))
+async def inference_instruct(tts_text: str = Form(), spk_id: str = Form(), instruct_text: str = Form(), stream: bool = Form(default=False), format: str = Form(default="pcm")):
+    model_output = cosyvoice.inference_instruct(tts_text, spk_id, instruct_text, stream=stream)
+    if format == "pcm":
+        return StreamingResponse(generate_data(model_output))
+    else:
+        return Response(build_data(model_output), media_type="audio/wav")
+
+
+@app.get("/inference_instruct_v2")
+async def inference_instruct_v2(tts_text: str = Form(), instruct_text: str = Form(), prompt_wav: UploadFile = File(), stream: bool = Form(default=False), format: str = Form(default="pcm")):
+    prompt_speech_16k = load_wav(prompt_wav.file, 16000)
+    model_output = cosyvoice.inference_instruct2(tts_text, instruct_text, prompt_speech_16k, stream=stream)
+    if format == "pcm":
+        return StreamingResponse(generate_data(model_output))
+    else:
+        return Response(build_data(model_output), media_type="audio/wav")
 
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--port',
                         type=int,
                         default=50000)
-    parser.add_argument('--model_dir',
-                        type=str,
-                        default='iic/CosyVoice-300M',
-                        help='local path or modelscope repo id')
     args = parser.parse_args()
-    cosyvoice = CosyVoice(args.model_dir)
     uvicorn.run(app, host="0.0.0.0", port=args.port)

webui.py

@@ -33,6 +33,7 @@
                  '自然语言控制': '1. 选择预训练音色\n2. 输入instruct文本\n3. 点击生成音频按钮'}
 stream_mode_list = [('否', False), ('是', True)]
 max_val = 0.8
+v2 = True
 
 
 def generate_seed():
@@ -128,8 +129,13 @@ def generate_audio(tts_text, mode_checkbox_group, sft_dropdown, prompt_text, pro
     else:
         logging.info('get instruct inference request')
         set_all_random_seed(seed)
-        for i in cosyvoice.inference_instruct(tts_text, sft_dropdown, instruct_text, stream=stream, speed=speed):
-            yield (cosyvoice.sample_rate, i['tts_speech'].numpy().flatten())
+        if v2:
+            prompt_speech_16k = postprocess(load_wav(prompt_wav, prompt_sr))
+            for i in cosyvoice.inference_instruct2(tts_text, instruct_text, prompt_speech_16k, stream=stream, speed=speed):
+                yield (cosyvoice.sample_rate, i['tts_speech'].numpy().flatten())
+        else:
+            for i in cosyvoice.inference_instruct(tts_text, sft_dropdown, instruct_text, stream=stream, speed=speed):
+                yield (cosyvoice.sample_rate, i['tts_speech'].numpy().flatten())
 
 
 def main():
@@ -159,7 +165,7 @@ def main():
 
         generate_button = gr.Button("生成音频")
 
-        audio_output = gr.Audio(label="合成音频", autoplay=True, streaming=True)
+        audio_output = gr.Audio(label="合成音频", autoplay=True, streaming=False)
 
         seed_button.click(generate_seed, inputs=[], outputs=seed)
         generate_button.click(generate_audio,
@@ -181,7 +187,7 @@ def main():
                         default='pretrained_models/CosyVoice2-0.5B',
                         help='local path or modelscope repo id')
     args = parser.parse_args()
-    cosyvoice = CosyVoice2(args.model_dir) if 'CosyVoice2' in args.model_dir else CosyVoice(args.model_dir)
+    cosyvoice, v2 = (CosyVoice2(args.model_dir),True) if 'CosyVoice2' in args.model_dir else (CosyVoice(args.model_dir),False)
     sft_spk = cosyvoice.list_avaliable_spks()
     prompt_sr = 16000
     default_data = np.zeros(cosyvoice.sample_rate)