Update app.py

app.py

@@ -38,6 +38,38 @@ from TTS.tts.models import setup_model
 from TTS.config import load_config
 from TTS.tts.models.vits import *  
 
+import whisper
+model = whisper.load_model("small")
+import os
+os.system('pip install voicefixer --upgrade')
+from voicefixer import VoiceFixer
+voicefixer = VoiceFixer()
+import gradio as gr
+import openai
+import torch
+import torchaudio
+from speechbrain.pretrained import SpectralMaskEnhancement
+
+enhance_model = SpectralMaskEnhancement.from_hparams(
+source="speechbrain/metricgan-plus-voicebank",
+savedir="pretrained_models/metricgan-plus-voicebank",
+run_opts={"device":"cuda"},
+)
+
+mes1 = [
+    {"role": "system", "content": "You are a TOEFL examiner. Help me improve my oral Englsih and give me feedback."}
+]
+
+mes2 = [
+    {"role": "system", "content": "You are a mental health therapist. Your name is Tina."}
+]
+
+mes3 = [
+    {"role": "system", "content": "You are my personal assistant. Your name is Alice."}
+]
+
+res = []
+
 OUT_PATH = 'out/'
 
 # create output path
@@ -103,8 +135,47 @@ def compute_spec(ref_file):
   
 
     
-def greet(Text,Voicetoclone,VoiceMicrophone):
-    text= "%s" % (Text)
+def greet(apikey,Voicetoclone,VoiceMicrophone, audio, choice1):
+
+    openai.api_key = apikey
+
+    audio = whisper.load_audio(audio)
+    audio = whisper.pad_or_trim(audio)
+
+    # make log-Mel spectrogram and move to the same device as the model
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+    # detect the spoken language
+    _, probs = model.detect_language(mel)
+    print(f"Detected language: {max(probs, key=probs.get)}")
+
+    # decode the audio
+    options = whisper.DecodingOptions()
+    result = whisper.decode(model, mel, options)
+    res.append(result.text)
+
+    if choice1 == "TOEFL":
+      messages = mes1
+    elif choice1 == "Therapist":
+      messages = mes2
+    elif choice1 == "Alice":
+      messages = mes3
+
+    # chatgpt
+    n = len(res)
+    content = res[n-1]
+    messages.append({"role": "user", "content": content})
+
+    completion = openai.ChatCompletion.create(
+      model = "gpt-3.5-turbo",
+      messages = messages
+    )
+
+    chat_response = completion.choices[0].message.content
+
+    messages.append({"role": "assistant", "content": chat_response})   
+    
+    text= chat_response
     if Voicetoclone is not None:
       reference_files= "%s" % (Voicetoclone)
       print("path url")
@@ -154,12 +225,40 @@ def greet(Text,Voicetoclone,VoiceMicrophone):
       out_path = os.path.join(OUT_PATH, file_name)
       print(" > Saving output to {}".format(out_path))
       ap.save_wav(wav, out_path)
-      return out_path
 
-demo = gr.Interface(
+      voicefixer.restore(input=out_path, # input wav file path
+                      output="audio1.wav", # output wav file path
+                      cuda=True, # whether to use gpu acceleration
+                      mode = 0) # You can try out mode 0, 1 to find out the best result
+
+
+
+      noisy = enhance_model.load_audio(
+      "audio1.wav"
+      ).unsqueeze(0)
+
+      enhanced = enhance_model.enhance_batch(noisy, lengths=torch.tensor([1.]))
+      torchaudio.save("enhanced.wav", enhanced.cpu(), 16000)
+
+      return [result.text, chat_response, "enhanced.wav"]
+
+output_1 = gr.Textbox(label="Speech to Text")
+output_2 = gr.Textbox(label="ChatGPT Output")
+output_3 = gr.Audio(label="Audio with Custom Voice")
+
+gr.Interface(
+    title = '🥳💬💕 - TalktoAI，随时随地，谈天说地！', 
+    theme="huggingface",
+    description = "🤖 - 让有人文关怀的AI造福每一个人！AI向善，文明璀璨！TalktoAI - Enable the future！",
     fn=greet, 
-    inputs=[gr.inputs.Textbox(label='What would you like the voice to say? (max. 2000 characters per request)'),gr.Audio(type="filepath",         source="upload",label='Please upload a voice to clone (max. 30mb)'),gr.Audio(source="microphone", type="filepath", streaming=True)],
-    outputs="audio",
-    title="Bilal's Voice Cloning Tool"
-    )
-demo.launch()
+    inputs=[
+        gr.Textbox(lines=1, label = "请填写您的OpenAI-API-key"),
+        gr.inputs.Audio(source="upload", label = "请上传您喜欢的声音(wav文件)", type="filepath"),
+        gr.inputs.Audio(source="microphone", label = "请用语音上传您喜欢的声音，语音和文件上传二选一即可", type="filepath"),
+        gr.inputs.Audio(source="microphone", type="filepath"),
+        gr.Radio(["TOEFL", "Therapist", "Alice"], label="TOEFL Examiner, Therapist Tina, or Assistant Alice?"),
+    ],
+    outputs=[
+      output_1, output_2, output_3
+    ],
+    ).launch()