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import gradio as gr
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import torch
import librosa
# Model details
models = {
"m3hrdadfi/wav2vec2-large-xlsr-persian-v3": None,
"jonatasgrosman/wav2vec2-large-xlsr-53-persian": None,
"AlirezaSaei/wav2vec2-large-xlsr-persian-fine-tuned": None
}
# Load models and processors
def load_model(model_name):
model = Wav2Vec2ForCTC.from_pretrained(model_name)
processor = Wav2Vec2Processor.from_pretrained(model_name)
return model, processor
def transcribe(audio, model_name):
if models[model_name] is None:
models[model_name] = load_model(model_name)
model, processor = models[model_name]
audio_data, _ = librosa.load(audio, sr=16000)
input_values = processor(audio_data, sampling_rate=16000, return_tensors="pt", padding=True).input_values
with torch.no_grad():
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
return transcription
# Gradio app
with gr.Blocks(theme="compact") as demo:
gr.Markdown("""
<h1 style="color: #4CAF50; text-align: center;">Persian Speech-to-Text Models</h1>
<p style="text-align: center;">Test the best Persian STT models in one place!</p>
""")
with gr.Row():
audio_input = gr.Audio(source="upload", type="filepath", label="Upload your audio file")
model_dropdown = gr.Dropdown(
choices=list(models.keys()),
label="Select Model",
value="m3hrdadfi/wav2vec2-large-xlsr-persian-v3"
)
output_text = gr.Textbox(label="Transcription", lines=5, placeholder="The transcription will appear here...")
transcribe_button = gr.Button("Transcribe", variant="primary")
transcribe_button.click(
fn=transcribe,
inputs=[audio_input, model_dropdown],
outputs=output_text
)
gr.Markdown("""
<footer style="text-align: center; margin-top: 20px;">
<p>Created with ❤️ using Gradio and Hugging Face</p>
</footer>
""")
demo.launch()
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