import tempfile import gradio as gr from neon_tts_plugin_coqui import CoquiTTS LANGUAGES = list(CoquiTTS.langs.keys()) default_lang = "en" # ChatGPT #from pyChatGPT import ChatGPT #import whisper #whisper_model = whisper.load_model("small") whisper = gr.Interface.load(name="spaces/sanchit-gandhi/whisper-large-v2") import os #session_token = os.environ.get('SessionToken') api = os.environ.get('API_ENDPOINT') title = "Speech to ChatGPT to Speech" #info = "more info at [Neon Coqui TTS Plugin](https://github.com/NeonGeckoCom/neon-tts-plugin-coqui), [Coqui TTS](https://github.com/coqui-ai/TTS)" #badge = "https://visitor-badge-reloaded.herokuapp.com/badge?page_id=neongeckocom.neon-tts-plugin-coqui" coquiTTS = CoquiTTS() def call_api(message): response = requests.get(f'{api}?q={message}') if response.status_code == 200: return str(response.text).split('\n', 2)[2] else: return """Sorry, I'm quite busy right now, but please try again later :)""" # ChatGPT def chat_hf(audio, custom_token, language): try: whisper_text = translate(audio) if whisper_text == "ERROR: You have to either use the microphone or upload an audio file": gpt_response = "MISSING AUDIO: Record your voice by clicking the microphone button, do not forget to stop recording before sending your message ;)" else: gpt_response = call_api(whisper_text) #whisper_text = translate(audio) #api = ChatGPT(session_token) #resp = api.send_message(whisper_text) #api.refresh_auth() # refresh the authorization token #api.reset_conversation() # reset the conversation #gpt_response = resp['message'] except: whisper_text = translate(audio) gpt_response = """Sorry, I'm quite busy right now, but please try again later :)""" #whisper_text = translate(audio) #api = ChatGPT(custom_token) #resp = api.send_message(whisper_text) #api.refresh_auth() # refresh the authorization token #api.reset_conversation() # reset the conversation #gpt_response = resp['message'] # to voice with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp: coquiTTS.get_tts(gpt_response, fp, speaker = {"language" : language}) return whisper_text, gpt_response, fp.name # whisper #def translate(audio): # print(""" # — # Sending audio to Whisper ... # — # """) # # audio = whisper.load_audio(audio) # audio = whisper.pad_or_trim(audio) # # mel = whisper.log_mel_spectrogram(audio).to(whisper_model.device) # # _, probs = whisper_model.detect_language(mel) # # transcript_options = whisper.DecodingOptions(task="transcribe", fp16 = False) # # transcription = whisper.decode(whisper_model, mel, transcript_options) # # print("language spoken: " + transcription.language) # print("transcript: " + transcription.text) # print("———————————————————————————————————————————") # # return transcription.text def translate(audio): print(""" — Sending audio to Whisper ... — """) text_result = whisper(audio, None, "transcribe", fn_index=0) #print(text_result) return text_result with gr.Blocks() as blocks: gr.Markdown("