Create app.py

app.py

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+from TTS.api import TTS
+tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=True)
+from voicefixer import VoiceFixer
+import whisper
+model = whisper.load_model("small")
+import gradio as gr
+import openai
+
+voicefixer = VoiceFixer()
+
+mes1 = [
+    {"role": "system", "content": "You are a TOEFL examiner. Help me improve my oral Englsih and give me feedback. Replace the Arabic numerals with the corresponding English words in your response."}
+]
+
+mes2 = [
+    {"role": "system", "content": "You are a mental health therapist. Your name is Tina. Replace the Arabic numerals with the corresponding English words in your response."}
+]
+
+mes3 = [
+    {"role": "system", "content": "You are my personal assistant. Your name is Alice. Replace the Arabic numerals with the corresponding English words in your response."}
+]
+
+res = []
+
+def transcribe(apikey, upload, audio, choice1):
+
+    openai.api_key = apikey
+    
+    # time.sleep(3)
+    # load audio and pad/trim it to fit 30 seconds
+    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})   
+
+    tts.tts_to_file(chat_response, speaker_wav = upload, language="en", file_path="output.wav")
+    
+    audio_in = "output.wav"
+
+    voicefixer.restore(input=audio_in, # input wav file path
+                    output="output1.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
+
+    return [result.text, chat_response, "output1.wav"]
+
+output_1 = gr.Textbox(label="Speech to Text")
+output_2 = gr.Textbox(label="ChatGPT Output")
+output_3 = gr.Audio(label="Audio")
+
+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=transcribe, 
+    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", 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()