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import os
import gradio as gr
from transformers import pipeline
demo = gr.Blocks()
pipe = pipeline("automatic-speech-recognition", model="jonatasgrosman/wav2vec2-large-xlsr-53-english")
pipe2 = pipeline("summarization", model="facebook/bart-large-cnn")
def launch(input):
out = pipe(input)
out2 = pipe2(out)
return out2[0]['summarized notes']
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"],
)
demo.launch(share=True,
server_port=int(os.environ['PORT1'])) |