| import whisper |
| import gradio as gr |
| import os |
| from pydub import AudioSegment |
| from transformers import BlenderbotTokenizer, BlenderbotForConditionalGeneration |
| from TTS.api import TTS |
| import torch |
|
|
| mname = "facebook/blenderbot-400M-distill" |
| chat_model = BlenderbotForConditionalGeneration.from_pretrained(mname) |
| tokenizer = BlenderbotTokenizer.from_pretrained(mname) |
|
|
| device = 'cuda' if torch.cuda.is_available() else 'cpu' |
|
|
| chat_model = chat_model.to(device) |
| SR_model = whisper.load_model("base") |
| SR_model = SR_model.to(device) |
|
|
| tts_model_name = TTS.list_models()[11] |
| tts = TTS(tts_model_name) |
|
|
| def querybot(audio_file): |
| AudioSegment.from_wav(audio_file).export("file.mp3", format="mp3") |
| UTTERANCE = SR_model.transcribe("file.mp3")["text"] |
|
|
| inputs = tokenizer([UTTERANCE], return_tensors="pt").to(device) |
| reply_ids = chat_model.generate(**inputs) |
| response = tokenizer.batch_decode(reply_ids, skip_special_tokens=True)[0] |
| tts.tts_to_file(text=response, file_path='tts.mp3') |
|
|
| output = response |
|
|
| return output, 'tts.mp3' |
|
|
|
|
| gr.Interface( |
| fn=querybot, |
| inputs=gr.Audio(source="microphone", type="filepath"), |
| outputs=['text',gr.Audio(type='filepath')]).launch() |
