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import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from datasets import load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "openai/whisper-large-v3-turbo"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
torch_dtype=torch_dtype,
device=device,
)
# dataset = load_dataset("distil-whisper/librispeech_long", "clean", split="validation")
# sample = dataset[0]["audio"]
# result = pipe(sample)
# transcript = result["text"]
import os
import gradio as gr
def launch(input):
out = pipe(input)
result = pipe(out[0])
transcript = result["text"]
# context_str = out[0]['generated_text']
# translate_str = translate(context_str, 'en', 'sq')
return translate_str
iface = gr.Interface(launch,
inputs=gr.Audio(label="Audio", source="microphone", type="filepath", elem_id='audio'),
outputs="text")
iface.launch(share=True)
# iface.launch(share=True,
# server_port=int(os.environ['PORT1']))
iface.close()
# def click_js():
# return """function audioRecord() {
# var xPathRes = document.evaluate ('//*[@id="audio"]//button', document, null, XPathResult.FIRST_ORDERED_NODE_TYPE, null);
# xPathRes.singleNodeValue.click();}"""
# def action(btn):
# """Changes button text on click"""
# if btn == 'Speak': return 'Stop'
# else: return 'Speak'
# def check_btn(btn):
# """Checks for correct button text before invoking transcribe()"""
# if btn != 'Speak': raise Exception('Recording...')
# def transcribe():
# return 'Success'
# with gr.Blocks() as demo:
# msg = gr.Textbox()
# audio_box = gr.Audio(label="Audio", source="microphone", type="filepath", elem_id='audio')
# with gr.Row():
# audio_btn = gr.Button('Speak')
# clear = gr.Button("Clear")
# audio_btn.click(fn=action, inputs=audio_btn, outputs=audio_btn).\
# then(fn=lambda: None, _js=click_js()).\
# then(fn=check_btn, inputs=audio_btn).\
# success(fn=transcribe, outputs=msg)
# clear.click(lambda: None, None, msg, queue=False)
# demo.queue().launch(debug=True)
# print(result["text"]) |