Spaces:
Running
Running
File size: 2,797 Bytes
438f69f 41af2be 438f69f 41af2be 438f69f 4336800 41af2be 438f69f 41af2be 70998e6 41af2be 70998e6 41af2be e80d53c 41af2be 438f69f 41af2be 438f69f 4336800 438f69f 41af2be 438f69f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
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()
|