Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,744 Bytes
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import os
import gradio as gr
import torch
import nemo.collections.asr as nemo_asr
from omegaconf import OmegaConf
import time
import spaces
# Check if CUDA is available
print(f"CUDA available: {torch.cuda.is_available()}")
if torch.cuda.is_available():
print(f"CUDA device: {torch.cuda.get_device_name(0)}")
model = nemo_asr.models.EncDecRNNTBPEModel.from_pretrained("nvidia/parakeet-tdt-0.6b-v2")
print(f"Model loaded on device: {model.device}")
@spaces.GPU(duration=120) # Increase duration if inference takes >60s
def transcribe(audio, state=""):
"""
Transcribe audio in real-time
"""
# Skip processing if no audio is provided
if audio is None:
return state, state
if isinstance(audio, tuple):
# If audio is a tuple, assume the first element is the file path
print("Received tuple input, extracting first element as file path")
audio = audio[0] if len(audio) > 0 else None
elif not isinstance(audio, str):
raise ValueError(f"Expected audio as a file path (str), got {type(audio)}")
if not audio:
raise ValueError("No valid audio input provided")
global model
# Move model to GPU if available
if torch.cuda.is_available():
print(f"CUDA device: {torch.cuda.get_device_name(0)}")
model = model.cuda()
# Get the sample rate from the audio
sample_rate = 16000 # Default to 16kHz if not specified
# Process the audio with the ASR model
with torch.no_grad():
transcription = model.transcribe([audio])[0]
# Append new transcription to the state
if state == "":
new_state = transcription
else:
new_state = state + " " + transcription
model.cpu()
return new_state, new_state
# Define the Gradio interface
with gr.Blocks(title="Real-time Speech-to-Text with NeMo") as demo:
gr.Markdown("# 🎙️ Real-time Speech-to-Text Transcription")
gr.Markdown("Powered by NVIDIA NeMo and the parakeet-tdt-0.6b-v2 model")
with gr.Row():
with gr.Column(scale=2):
audio_input = gr.Audio(
sources=["microphone"],
type="numpy",
streaming=True,
label="Speak into your microphone"
)
clear_btn = gr.Button("Clear Transcript")
with gr.Column(scale=3):
text_output = gr.Textbox(
label="Transcription",
placeholder="Your speech will appear here...",
lines=10
)
streaming_text = gr.Textbox(
label="Real-time Transcription",
placeholder="Real-time results will appear here...",
lines=2
)
# State to store the ongoing transcription
state = gr.State("")
# Handle the audio stream
audio_input.stream(
fn=transcribe,
inputs=[audio_input, state],
outputs=[state, streaming_text],
)
# Clear the transcription
def clear_transcription():
return "", "", ""
clear_btn.click(
fn=clear_transcription,
inputs=[],
outputs=[text_output, streaming_text, state]
)
# Update the main text output when the state changes
state.change(
fn=lambda s: s,
inputs=[state],
outputs=[text_output]
)
gr.Markdown("## 📝 Instructions")
gr.Markdown("""
1. Click the microphone button to start recording
2. Speak clearly into your microphone
3. The transcription will appear in real-time
4. Click 'Clear Transcript' to start a new transcription
""")
# Launch the app
if __name__ == "__main__":
demo.launch()
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