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import gradio as gr
import numpy as np
from difflib import Differ
# import spaces #[uncomment to use ZeroGPU]
import torch
from transformers import pipeline
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "emlinking/wav2vec2-large-xls-r-300m-tsm-asr-v6"
if torch.cuda.is_available():
torch_dtype = torch.float16
else:
torch_dtype = torch.float32
pipe = pipeline(task="automatic-speech-recognition", model=model_repo_id, device=device)
# @spaces.GPU #[uncomment to use ZeroGPU]
def infer(
audio,
target
):
sampling_rate, wav = audio
if wav.ndim > 1:
wav = wav.mean(axis=1)
wav = wav.astype(np.float32)
wav /= np.max(np.abs(wav))
user_pron = pipe(wav)['text']
# compare texts
d = Differ()
d_toks = [(i[2:], i[0] if i[0] != " " else None) for i in d.compare(target, user_pron)]
return (user_pron, d_toks)
css = """
#col-container {
margin: 0 auto;
max-width: 640px;
}
"""
with gr.Blocks(css=css) as demo:
gr.Markdown(" # PhonoLearn")
target = gr.Textbox(label='Practice Sentence (Tâi-lô)')
input_audio = gr.Audio(
sources=["microphone", "upload"]
)
output = gr.Textbox(label='Your Pronunciation')
diff = gr.HighlightedText(
label='Comparison',
combine_adjacent=True,
show_legend=True,
color_map={'+': 'red', '-': 'green'}
)
input_audio.input(fn=infer, inputs=[input_audio, target], outputs=[output, diff])
if __name__ == "__main__":
demo.launch()
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