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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) | |
| 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 = [] | |
| 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") | |
| voicefixer.restore(input="output.wav", # 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=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(wav文件)", 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() |