Spaces:
Sleeping
Sleeping
File size: 2,435 Bytes
3f95fab 1134893 e80497f 3f95fab 1134893 e80497f 3f95fab e4cfdaf 3f95fab e80497f 3f95fab e80497f 3f95fab e80497f 3f95fab e80497f 3f95fab e80497f |
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 |
import torch
import torchaudio
from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq, pipeline
import gradio as gr
# Setup device
DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
WHISPER_SAMPLE_RATE = 16000
# Load model and processor
model_id = "ixxan/whisper-small-thugy20"
processor = AutoProcessor.from_pretrained("ixxan/whisper-small-thugy20")
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(DEVICE)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
torch_dtype=torch_dtype,
device=DEVICE,
)
def preprocess_audio(audio_path: str) -> torch.Tensor:
try:
audio, sample_rate = torchaudio.load(audio_path)
# Resample if necessary
if sample_rate != WHISPER_SAMPLE_RATE:
resampler = torchaudio.transforms.Resample(
orig_freq=sample_rate, new_freq=WHISPER_SAMPLE_RATE
)
audio = resampler(audio)
# Convert to mono
if audio.shape[0] > 1:
audio = torch.mean(audio, dim=0)
return audio.squeeze()
except Exception as e:
raise RuntimeError(f"Error processing audio file: {str(e)}")
def transcribe(audio_path):
try:
if audio_path is None:
return "No audio provided. Please record or upload an audio file."
audio_input = preprocess_audio(audio_path)
input_features = processor(
audio_input,
sampling_rate=WHISPER_SAMPLE_RATE,
return_tensors="pt",
).input_features.to(DEVICE)
predicted_ids = model.generate(input_features)
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
return transcription
except Exception as e:
return f"Error transcribing audio: {str(e)}"
# Create Gradio interface
iface = gr.Interface(
fn=transcribe,
inputs=gr.Audio(type="filepath"),
outputs="text",
title="Uyghur Speech Recognition",
description="Upload or record audio in Uyghur to get its transcription.",
examples=[], # You can add example audio files here if you have them
)
# Launch the app
if __name__ == "__main__":
iface.launch() |