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from transformers.utils import logging
logging.set_verbosity_error()
# from datasets import load_dataset
from transformers import pipeline
import gradio as gr
# dataset = load_dataset("librispeech_asr",
# split="train.clean.100",
# streaming=True,
# trust_remote_code=True)
asr = pipeline(task="automatic-speech-recognition",
model="openai/whisper-small")
demo = gr.Blocks()
def transcribe_long_form(filepath):
if filepath is None:
gr.Warning("No audio found, please retry.")
return ""
output = asr(
filepath,
max_new_tokens=256,
chunk_length_s=30,
batch_size=8,
)
return output["text"]
mic_transcribe = gr.Interface(
fn=transcribe_long_form,
inputs=gr.Audio(sources="microphone",
type="filepath"),
outputs=gr.Textbox(label="Transcription",
lines=3),
allow_flagging="never")
file_transcribe = gr.Interface(
fn=transcribe_long_form,
inputs=gr.Audio(sources="upload",
type="filepath"),
outputs=gr.Textbox(label="Transcription",
lines=3),
allow_flagging="never",
)
with demo:
gr.TabbedInterface(
[mic_transcribe,
file_transcribe],
["Transcribe Microphone",
"Transcribe Audio File"], title= "Automatic Speech Recognition"
)
demo.launch(share=True) |