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| import gradio as gr | |
| import whisper | |
| import numpy as np | |
| import openai | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| with open('app.css','r') as f: | |
| css_file = f.read() | |
| markdown=""" | |
| # Polish ASR BIGOS workspace | |
| """ | |
| def whisper_model_change(radio_whisper_model): | |
| whisper_model = whisper.load_model(radio_whisper_model) | |
| return(whisper_model) | |
| def prompt_gpt(input_text): | |
| messages = [ | |
| {"role": "system", "content": "You are a helpful assistant."}] | |
| if input_text: | |
| messages.append( | |
| {"role": "user", "content": input_text}, | |
| ) | |
| chat_completion = openai.ChatCompletion.create( | |
| model="gpt-3.5-turbo", messages=messages | |
| ) | |
| reply = chat_completion.choices[0].message.content | |
| return reply | |
| def process_pipeline(audio): | |
| asr_out = transcribe(audio) | |
| gpt_out = prompt_gpt(asr_out) | |
| tts_out = synthesize_speech(gpt_out) | |
| return(tts_out) | |
| def transcribe(audio, language, whisper_model, whisper_model_type): | |
| if not whisper_model: | |
| whisper_model=init_whisper_model(whisper_model_type) | |
| print(f"Transcribing {audio} for language {language} and model {whisper_model_type}") | |
| audio = whisper.load_audio(audio) | |
| audio = whisper.pad_or_trim(audio) | |
| mel = whisper.log_mel_spectrogram(audio) | |
| options = whisper.DecodingOptions(language=language, without_timestamps=True, fp16=False) | |
| result = whisper.decode(whisper_model, mel, options) | |
| result_text = result.text | |
| return result_text | |
| def init_whisper_model(whisper_model_type): | |
| print("Initializing whisper model") | |
| print(whisper_model_type) | |
| whisper_model = whisper.load_model(whisper_model_type) | |
| return whisper_model | |
| def synthesize_speech(text): | |
| audioobj = gTTS(text = out_result, | |
| lang = lang, | |
| slow = False) | |
| audioobj.save("Temp.mp3") | |
| return("Temp.mp3") | |
| block = gr.Blocks(css=css_file) | |
| with block: | |
| #state variables | |
| language = gr.State("en") | |
| whisper_model_type = gr.State("base") | |
| whisper_model = gr.State() | |
| # state handling functions | |
| def change_language(choice): | |
| if choice == "Polish": | |
| language="pl" | |
| print("Switching to Polish") | |
| print("language") | |
| print(language) | |
| elif choice == "English": | |
| language="en" | |
| print("Switching to English") | |
| print("language") | |
| print(language) | |
| return(language) | |
| def change_whisper_model(choice): | |
| whisper_model_type = choice | |
| print("Switching Whisper model") | |
| print(whisper_model_type) | |
| whisper_model = init_whisper_model(whisper_model_type) | |
| return [whisper_model_type, whisper_model] | |
| gr.Markdown(markdown) | |
| with gr.Tabs(): | |
| with gr.TabItem('Voicebot playground'): | |
| with gr.Box(): | |
| gr.HTML("<p class=\"apikey\">API Key:</p>") | |
| # API key textbox (password-style) | |
| api_key = gr.Textbox(label="", elem_id="pw") | |
| radio_lang = gr.Radio(["Polish", "English"], label="Language", info="If none selected, English is used") | |
| #radio_asr_type = gr.Radio(["Local", "Cloud"], label="Select ASR type", info="Cloud models are faster and more accurate, but costs money") | |
| #radio_cloud_asr = gr.Radio(["Whisper", "Google", "Azure"], label="Select Cloud ASR provider", info="You need to provide API keys for specific service") | |
| radio_whisper_model = gr.Radio(["tiny", "base", "small", "medium", "large"], label="Whisper ASR model (local)", info="Larger models are better, but slower. Default - base") | |
| mic_recording = gr.Audio(source="microphone", type="filepath", label='Record your voice') | |
| out_asr = gr.Textbox(placeholder="ASR output", | |
| lines=5, | |
| max_lines=10, | |
| show_label=False) | |
| out_gpt = gr.Textbox(placeholder="ChatGPT output", | |
| lines=10, | |
| max_lines=25, | |
| show_label=False) | |
| button_transcribe = gr.Button("Transcribe") | |
| button_prompt_gpt = gr.Button("Prompt ChatGPT") | |
| button_transcribe.click(transcribe, inputs=[mic_recording,language, whisper_model,whisper_model_type], outputs=out_asr) | |
| button_prompt_gpt.click(prompt_gpt, inputs=out_asr, outputs=out_gpt) | |
| radio_lang.change(fn=change_language, inputs=radio_lang, outputs=language) | |
| radio_whisper_model.change(fn=change_whisper_model, inputs=radio_whisper_model, outputs=[whisper_model_type, whisper_model]) | |
| block.launch() |