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from gtts import gTTS
from transformers import pipeline
import gradio as gr
import uuid
asr = pipeline('automatic-speech-recognition', "facebook/wav2vec2-conformer-rope-large-960h-ft")
corrector = pipeline("text2text-generation", model="pszemraj/grammar-synthesis-small")
transcribe = lambda audio: asr(audio)['text'].lower()
def to_audio(s):
audio_path = f'/tmp/{uuid.uuid4()}.mp3'
tts = gTTS(s, tld='us')
tts.save(audio_path)
return audio_path
#@title English Learning
def transcription(audio, history):
if audio:
message = transcribe(audio)
history.append(( (audio, ) , message))
results = corrector(message)
results = '\n'.join([t['generated_text'] for t in results])
history.append( (None, f'**[Grammar and examples]**\n {results}') )
return history
def chat(message, history):
audio_path = to_audio(message)
history.append((message, (audio_path,)))
results = corrector(message)
results = '\n'.join([t['generated_text'] for t in results])
history.append( (None, f'**[Grammar and examples]**\n {results}') )
return None, history
with gr.Blocks(theme=gr.themes.Soft()) as learning:
gr.Markdown('# The main aim of this app is to help English learners to speak fluently.')
chatbot = gr.Chatbot()
with gr.Row():
message = gr.Textbox(label='Send your message to TTS')
microphone = gr.Audio(label="Transcribe", source="microphone", type="filepath")
microphone.change(transcription, [microphone, chatbot], [chatbot])
microphone.change(lambda:None, None, microphone)
message.submit(chat, [message, chatbot], [message, chatbot])
learning.launch()