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Running
on
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Running
on
Zero
import os | |
import spaces | |
import torch | |
import gradio as gr | |
from transformers import pipeline | |
MODEL_NAME = "openai/whisper-large-v3" | |
BATCH_SIZE = 8 | |
FILE_LIMIT_MB = 1000 | |
device = 0 if torch.cuda.is_available() else "cpu" | |
pipe = pipeline( | |
task="automatic-speech-recognition", | |
model=MODEL_NAME, | |
chunk_length_s=30, | |
device=device, | |
) | |
def respond_to_question_llama(transcript, question): | |
from huggingface_hub import InferenceClient | |
client = InferenceClient( | |
"meta-llama/Meta-Llama-3.1-70B-Instruct", | |
token=os.environ["HUGGINGFACEHUB_API_TOKEN"], | |
) | |
response = client.chat_completion( | |
messages=[{"role": "user", "content": f"Transcript: {transcript}\n\nUser: {question}"}], | |
max_tokens=4096, | |
).choices[0].message.content | |
return response | |
def audio_transcribe(inputs): | |
status=True | |
text="Arquivo de audio nao carregado!" | |
status=False | |
if inputs is None: | |
raise gr.Error("No audio file submitted! Please upload an audio file before submitting your request.") | |
else: | |
text = pipe(inputs, batch_size=BATCH_SIZE, return_timestamps=True)['text'] | |
status = True | |
return [text, gr.Textbox(visible=status),gr.Textbox(visible=status),gr.Textbox(visible=status)] | |
def hidden_ask_question(): | |
return [gr.Textbox(visible=False),gr.Textbox(visible=False),gr.Textbox(visible=False)] | |
with gr.Blocks() as transcriberUI: | |
gr.Markdown( | |
""" | |
# Ola! | |
Clique no botao abaixo para selecionar o Audio que deseja conversar! | |
Ambiente disponivel 24x7. Running on ZeroGPU with openai/whisper-large-v3 | |
""" | |
) | |
inp = gr.Audio(sources="upload", type="filepath", label="Audio file") | |
transcribe = gr.Textbox(label="Transcricao", show_label=True, show_copy_button=True) | |
ask_question = gr.Textbox(label="Ask a question", visible=False) | |
response_output = gr.Textbox(label="Response", visible=False) | |
submit_question = gr.Button("Submit question", visible=False) | |
submit_button = gr.Button("Transcribe to Chat", variant='primary', size='sm') | |
clear_button = gr.ClearButton([transcribe,response_output,inp, ask_question]) | |
def ask_question_callback(transcription,question): | |
if ask_question: | |
response = respond_to_question_llama(transcription, question) | |
else: | |
response = "No question asked" | |
return response | |
#inp.upload(audio_transcribe, inputs=inp, outputs=[transcribe,ask_question,submit_question, response_output]) | |
submit_button.click(audio_transcribe, inputs=inp, outputs=[transcribe,ask_question,submit_question, response_output]) | |
submit_question.click(ask_question_callback, outputs=[response_output], inputs=[transcribe, ask_question]) | |
clear_button.click(hidden_ask_question,outputs=[ask_question,response_output,submit_question]) | |
transcriberUI.queue().launch() |