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"""
Wav2Vec2 XLS-R 1B Portuguese - Hugging Face Space
"""
import gradio as gr
import torch
import librosa
import numpy as np
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import warnings
warnings.filterwarnings("ignore")
# Initialize model and processor
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_name = "jonatasgrosman/wav2vec2-xls-r-1b-portuguese"
print(f"Loading model {model_name}...")
processor = Wav2Vec2Processor.from_pretrained(model_name)
model = Wav2Vec2ForCTC.from_pretrained(model_name)
model.to(device)
model.eval()
print(f"Model loaded on device: {device}")
def transcribe_audio(audio_path):
"""Transcribe audio using Wav2Vec2"""
try:
# Load and preprocess audio
speech_array, sampling_rate = librosa.load(audio_path, sr=16000, mono=True)
# Process with model
inputs = processor(
speech_array,
sampling_rate=16000,
return_tensors="pt",
padding=True
)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
logits = model(**inputs).logits
# Decode
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.decode(predicted_ids[0])
# Calculate confidence
probs = torch.softmax(logits, dim=-1)
confidence = torch.max(probs).item()
return transcription, confidence
except Exception as e:
return f"Error: {str(e)}", 0.0
def process_audio(audio):
"""Process audio input from Gradio"""
if audio is None:
return "Please provide an audio file.", ""
transcription, confidence = transcribe_audio(audio)
# Format output
output = f"**Transcription:** {transcription}\n\n"
output += f"**Confidence:** {confidence:.2%}"
return output, transcription
# Create Gradio interface
with gr.Blocks(title="Wav2Vec2 XLS-R 1B Portuguese") as demo:
gr.Markdown("# 🎙️ Wav2Vec2 XLS-R 1B - Portuguese ASR")
gr.Markdown("Speech recognition for Portuguese using jonatasgrosman/wav2vec2-xls-r-1b-portuguese")
with gr.Row():
with gr.Column():
audio_input = gr.Audio(
sources=["upload", "microphone"],
type="filepath",
label="Audio Input"
)
submit_btn = gr.Button("Transcribe", variant="primary")
with gr.Column():
output_text = gr.Markdown(label="Results")
transcription_output = gr.Textbox(
label="Transcription Text",
lines=3,
interactive=False
)
submit_btn.click(
fn=process_audio,
inputs=[audio_input],
outputs=[output_text, transcription_output]
)
# Examples section removed - was causing FileNotFoundError
# Launch the app - let Hugging Face Spaces handle the configuration
if __name__ == "__main__":
demo.launch() # Remove server_name and server_port for HF Spaces compatibility |