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import gradio as gr
import librosa
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
# config
model_name = "vumichien/wav2vec2-xls-r-1b-japanese"
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)
def process_audio_file(file):
data, sr = librosa.load(file)
if sr != 16000:
data = librosa.resample(data, sr, 16000)
print(data.shape)
inputs = processor(data, sampling_rate=16000, return_tensors="pt", padding=True)
return inputs
def transcribe(file, state=""):
inputs = process_audio_file(file)
with torch.no_grad():
output_logit = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
pred_ids = torch.argmax(output_logit, dim=-1)
text = processor.batch_decode(pred_ids)[0]
state += text + " "
return state, state
description = "A simple interface to transcribe from spoken Japanese to Hiragana."
article = "Author: <a href=\"https://huggingface.co/vumichien\">Vu Minh Chien</a>."
inputs = [gr.inputs.Audio(source="microphone", type="filepath", optional=True),
"state"]
outputs = ["textbox", "state"]
examples = [["samples/BASIC5000_0001.wav"],
["samples/BASIC5000_0005.wav"]
]
iface = gr.Interface(
fn=transcribe,
inputs=inputs,
outputs=outputs,
layout="horizontal",
theme="huggingface",
title="Transcribe Japanese audio to Hiragana",
description=description,
article=article,
allow_flagging='never',
examples=examples,
live=True,
)
iface.launch(enable_queue=True, share=True)
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