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import gradio as gr | |
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor | |
import torch | |
import librosa | |
import os | |
# 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 | |
# Read HTML banner | |
if os.path.exists("banner.html"): | |
with open("banner.html", "r", encoding="utf-8") as file: | |
banner = file.read() | |
else: | |
banner = "<h1 style='color: red; text-align: center;'>Banner file not found!</h1>" | |
# Gradio app | |
with gr.Blocks() as demo: | |
gr.HTML(banner) | |
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(): | |
with gr.Column(): | |
# Audio input (upload or record) | |
audio_input = gr.Audio( | |
type="filepath", label="Upload or Record Audio" | |
) | |
model_dropdown = gr.Dropdown( | |
choices=list(models.keys()), | |
label="Select Model", | |
value="m3hrdadfi/wav2vec2-large-xlsr-persian-v3" | |
) | |
# Add Test Audio Button | |
def use_test_audio(): | |
return "Test-Audio.ogg" | |
test_audio_button = gr.Button("Use Test Audio") | |
with gr.Column(): | |
output_text = gr.Textbox( | |
label="Transcription", lines=5, placeholder="The transcription will appear here..." | |
) | |
transcribe_button = gr.Button("Transcribe") | |
test_audio_button.click( | |
fn=use_test_audio, | |
inputs=[], | |
outputs=[audio_input] | |
) | |
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() | |