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import gradio as gr
import torch
from wenet.cli.model import load_model
def process_cat_embs(cat_embs):
device = "cpu"
cat_embs = torch.tensor(
[float(c) for c in cat_embs.split(',')]).to(device)
return cat_embs
def download_rev_models():
from huggingface_hub import hf_hub_download
import joblib
REPO_ID = "Revai/reverb-asr"
files = ['reverb_asr_v1.jit.zip', 'tk.units.txt']
downloaded_files = [hf_hub_download(repo_id=REPO_ID, filename=f) for f in files]
model = load_model(downloaded_files[0], downloaded_files[1])
return model
model = download_rev_models()
def recognition(audio, style=0):
if audio is None:
return "Input Error! Please enter one audio!"
cat_embs = ','.join([str(s) for s in (style, 1-style)])
cat_embs = process_cat_embs(cat_embs)
ans = model.transcribe(audio, cat_embs = cat_embs)
if ans is None:
return "ERROR! No text output! Please try again!"
txt = ans['text']
txt = txt.replace('▁', ' ')
return txt
# input
inputs = [
gr.inputs.Audio(source="microphone", type="filepath", label='Input audio'),
gr.Slider(0, 1, value=0, label="Verbatimicity - from non-verbatim (0) to verbatim (1)", info="Choose a transcription style between non-verbatim and verbatim"),
]
output = gr.outputs.Textbox(label="Output Text")
text = "ASR Transcription Opensource Demo"
# description
description = (
" Opensource Automatic Speech Recognition in English
Verbatim Transcript style(1) refers to word to word-to-word transcription of an audio
Non Verbatim Transcript style(0) refers to just conserving the message of the original audio
"
)
interface = gr.Interface(
fn=recognition,
inputs=inputs,
outputs=output,
title=text,
description=description,
theme='huggingface',
)
interface.launch(enable_queue=True)
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