Spaces:
Runtime error
Runtime error
File size: 2,341 Bytes
99b670d |
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 |
import gradio as gr
import numpy as np
import torch
import torchaudio
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq, WhisperTokenizer
# Define paths to the model and processor
model_name = "userdata/whisper-largeV2-03-ms-v11-LORA-Merged"
# Load the processor and model
processor = AutoProcessor.from_pretrained(model_name)
tokenizer = WhisperTokenizer.from_pretrained(model_name)
model = AutoModelForSpeechSeq2Seq.from_pretrained(model_name)
# Check and set the device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Function to chunk the audio
def chunk_audio(audio, chunk_length):
num_chunks = len(audio) // chunk_length + (1 if len(audio) % chunk_length > 0 else 0)
return [audio[i * chunk_length:(i + 1) * chunk_length] for i in range(num_chunks)]
# Function to transcribe an audio file
def transcribe(audio_path, chunk_length=16000 * 30): # 30 seconds chunks
# Load audio
speech_array, sampling_rate = torchaudio.load(audio_path)
# Resample to 16kHz
resampler = torchaudio.transforms.Resample(sampling_rate, 16000)
speech = resampler(speech_array).squeeze().numpy()
# Chunk the audio if it's too long
chunks = chunk_audio(speech, chunk_length)
# Transcribe each chunk
transcriptions = []
for chunk in chunks:
# Process the audio
inputs = processor(chunk, sampling_rate=16000, return_tensors="pt")
inputs = {key: value.to(device).to(torch.float16) for key, value in inputs.items()} # Convert to float16
# Generate token IDs
with torch.no_grad():
generated_ids = model.generate(inputs["input_features"], max_length=448)
# Decode the token IDs to text
transcription = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
transcriptions.append(transcription)
# Combine transcriptions
full_transcription = ' '.join(transcriptions)
return full_transcription
# Create the Gradio interface
iface = gr.Interface(
fn=transcribe, # Update to match the function name
inputs=gr.Audio(type="filepath"),
outputs=gr.Textbox(),
title="Audio Transcription App",
description="Upload an audio file to get a transcription."
)
# Launch the Gradio interface
iface.launch()
|