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CosyVoice

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iflamed commited on Jul 10

Commit

a4ab4ea

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1 Parent(s): 3513376

support upload audio

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Files changed (2) hide show

main.py +0 -40
runtime/python/fastapi_server.py +102 -0

main.py DELETED Viewed

@@ -1,40 +0,0 @@
-import io,time
-from fastapi import FastAPI, Response
-from fastapi.responses import HTMLResponse
-from cosyvoice.cli.cosyvoice import CosyVoice
-import torchaudio
-cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-SFT')
-# sft usage
-print(cosyvoice.list_avaliable_spks())
-app = FastAPI()
-@app.get("/api/voice/tts")
-async def tts(query: str, role: str):
-    start = time.process_time()
-    output = cosyvoice.inference_sft(query, role)
-    end = time.process_time()
-    print("infer time:", end-start, "seconds")
-    buffer = io.BytesIO()
-    torchaudio.save(buffer, output['tts_speech'], 22050, format="wav")
-    buffer.seek(0)
-    return Response(content=buffer.read(-1), media_type="audio/wav")
-@app.get("/api/voice/roles")
-async def roles():
-    return {"roles": cosyvoice.list_avaliable_spks()}
-@app.get("/", response_class=HTMLResponse)
-async def root():
-    return """
-    <!DOCTYPE html>
-    <html lang=zh-cn>
-        <head>
-            <meta charset=utf-8>
-            <title>Api information</title>
-        </head>
-        <body>
-            Get the supported tones from the Roles API first, then enter the tones and textual content in the TTS API for synthesis. <a href='./docs'>Documents of API</a>
-        </body>
-    </html>
-    """

runtime/python/fastapi_server.py ADDED Viewed

	@@ -0,0 +1,102 @@

+import os
+import sys
+import io,time
+from fastapi import FastAPI, Response, File, UploadFile, Form
+from fastapi.responses import HTMLResponse
+from contextlib import asynccontextmanager
+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
+from cosyvoice.utils.file_utils import load_wav
+import numpy as np
+import torch
+import torchaudio
+import logging
+logging.getLogger('matplotlib').setLevel(logging.WARNING)
+class LaunchFailed(Exception):
+    pass
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    model_dir = os.getenv("MODEL_DIR", "pretrained_models/CosyVoice-300M-SFT")
+    if model_dir:
+        logging.info("MODEL_DIR is {}", model_dir)
+        app.cosyvoice = CosyVoice('../../'+model_dir)
+        # sft usage
+        logging.info("Avaliable speakers {}", app.cosyvoice.list_avaliable_spks())
+    else:
+        raise LaunchFailed("MODEL_DIR environment must set")
+    yield
+app = FastAPI(lifespan=lifespan)
+def buildResponse(output):
+    buffer = io.BytesIO()
+    torchaudio.save(buffer, output, 22050, format="wav")
+    buffer.seek(0)
+    return Response(content=buffer.read(-1), media_type="audio/wav")
+@app.post("/api/inference/sft")
+@app.get("/api/inference/sft")
+async def sft(tts: str = Form(), role: str = Form()):
+    start = time.process_time()
+    output = app.cosyvoice.inference_sft(tts, role)
+    end = time.process_time()
+    logging.info("infer time is {} seconds", end-start)
+    return buildResponse(output['tts_speech'])
+@app.post("/api/inference/zero-shot")
+async def zeroShot(tts: str = Form(), prompt: str = Form(), audio: UploadFile = File()):
+    start = time.process_time()
+    prompt_speech = load_wav(audio.file, 16000)
+    prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes()
+    prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(prompt_audio, dtype=np.int16))).unsqueeze(dim=0)
+    prompt_speech_16k = prompt_speech_16k.float() / (2**15)
+    output = app.cosyvoice.inference_zero_shot(tts, prompt, prompt_speech_16k)
+    end = time.process_time()
+    logging.info("infer time is {} seconds", end-start)
+    return buildResponse(output['tts_speech'])
+@app.post("/api/inference/cross-lingual")
+async def crossLingual(tts: str = Form(), audio: UploadFile = File()):
+    start = time.process_time()
+    prompt_speech = load_wav(audio.file, 16000)
+    prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes()
+    prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(prompt_audio, dtype=np.int16))).unsqueeze(dim=0)
+    prompt_speech_16k = prompt_speech_16k.float() / (2**15)
+    output = app.cosyvoice.inference_cross_lingual(tts, prompt_speech_16k)
+    end = time.process_time()
+    logging.info("infer time is {} seconds", end-start)
+    return buildResponse(output['tts_speech'])
+@app.post("/api/inference/instruct")
+@app.get("/api/inference/instruct")
+async def instruct(tts: str = Form(), role: str = Form(), instruct: str = Form()):
+    start = time.process_time()
+    output = app.cosyvoice.inference_instruct(tts, role, instruct)
+    end = time.process_time()
+    logging.info("infer time is {} seconds", end-start)
+    return buildResponse(output['tts_speech'])
+@app.get("/api/roles")
+async def roles():
+    return {"roles": app.cosyvoice.list_avaliable_spks()}
+@app.get("/", response_class=HTMLResponse)
+async def root():
+    return """
+    <!DOCTYPE html>
+    <html lang=zh-cn>
+        <head>
+            <meta charset=utf-8>
+            <title>Api information</title>
+        </head>
+        <body>
+            Get the supported tones from the Roles API first, then enter the tones and textual content in the TTS API for synthesis. <a href='./docs'>Documents of API</a>
+        </body>
+    </html>
+    """