增加保存音频模型接口

api.py

@@ -6,6 +6,9 @@
 sys.path.append('{}/third_party/Matcha-TTS'.format(ROOT_DIR))
 ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
 
+import requests
+from pydub import AudioSegment
+
 import numpy as np
 from flask import Flask, request, Response,send_from_directory
 import torch
@@ -19,6 +22,8 @@
 from flask_cors import CORS
 from flask import make_response
 
+import shutil
+
 import json
 
 cosyvoice = CosyVoice('pretrained_models/CosyVoice-300M-25Hz')
@@ -41,6 +46,38 @@
 CORS(app, supports_credentials=True)
 
 
+def download_and_convert(mp3_url, wav_filename):
+    """Downloads an MP3 file and converts it to WAV.
+
+    Args:
+        mp3_url: The URL of the MP3 file.
+        wav_filename: The desired filename for the WAV file (including .wav extension).
+    """
+    try:
+        response = requests.get(mp3_url, stream=True)
+        response.raise_for_status()  # Raise an exception for bad status codes (4xx or 5xx)
+
+        with open("temp.mp3", "wb") as f:
+            for chunk in response.iter_content(chunk_size=8192):
+                f.write(chunk)
+
+        # Convert MP3 to WAV using pydub
+        sound = AudioSegment.from_mp3("temp.mp3")
+        sound.export(wav_filename, format="wav")
+
+        print(f"音频已成功下载并转换为 {wav_filename}")
+
+    except requests.exceptions.RequestException as e:
+        print(f"下载音频时出错: {e}")
+    except Exception as e:
+        print(f"转换音频时出错: {e}")
+    finally:
+        # Clean up the temporary MP3 file
+        import os
+        try:
+            os.remove("temp.mp3")
+        except OSError as e:
+            print(f"删除临时文件时出错: {e}")
 def speed_change(input_audio: np.ndarray, speed: float, sr: int):
     # 检查输入数据类型和声道数
     if input_audio.dtype != np.int16:
@@ -122,6 +159,37 @@ def generate():
         response.headers['Content-Disposition'] = 'attachment; filename=sound.ogg'
         return response
 
+@app.route("/save_voice", methods=['GET'])
+def save_voice():
+
+    text = request.args.get('text')
+    audio = request.args.get('audio')
+    voice_name = request.args.get('voice_name')
+
+
+    download_and_convert(audio,"zero_test.wav")
+
+    prompt_speech_16k = load_wav('zero_test.wav', 16000)
+    tts_speeches = []
+    for i, j in enumerate(cosyvoice.inference_zero_shot(text,text, prompt_speech_16k, stream=False)):
+        # torchaudio.save('sft_{}.wav'.format(i), j['tts_speech'], 22050)
+        tts_speeches.append(j['tts_speech'])
+
+    audio_data = torch.concat(tts_speeches, dim=1)
+    torchaudio.save('zero_shot.wav',audio_data, 22050, format="wav")
+
+    shutil.copyfile(f"{ROOT_DIR}/output.pt",f"{ROOT_DIR}/voices/{voice_name}.pt")
+
+    response = app.response_class(
+        response=json.dumps({"voice_name":voice_name}),
+        status=200,
+        mimetype='application/json'
+    )
+    return response
+
+
+
+
 
 @app.route("/", methods=['GET'])
 def sft_get():