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# Copyright (c) 2022 Horizon Robotics. (authors: Binbin Zhang)
# 2022 Chengdong Liang (liangchengdong@mail.nwpu.edu.cn)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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!"
# NOTE: model supports 16k sample_rate
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"),
]
examples = [
['examples/POD1000000012_S0000335.wav'],
['examples/POD1000000013_S0000062.wav'],
['examples/POD1000000032_S0000020.wav'],
['examples/POD1000000032_S0000038.wav'],
['examples/POD1000000032_S0000050.wav'],
['examples/POD1000000032_S0000058.wav'],
]
output = gr.outputs.Textbox(label="Output Text")
text = "Reverb ASR Transcription Styles Demo"
# description
description = (
"Reverb ASR supports verbatim and non-verbatim transcription. Try recording an audio with disfluencies (ex: \'uh\', \'um\') and testing both transcription styles. Or, choose an example audio below." # noqa
)
article = (
"<p style='text-align: center'>"
"<a href='https://rev.com' target='_blank'>Learn more about Rev</a>" # noqa
"</p>")
interface = gr.Interface(
fn=recognition,
inputs=inputs,
outputs=output,
title=text,
description=description,
article=article,
examples=examples,
)
interface.launch(enable_queue=True) |