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import torch | |
import torchaudio | |
import gradio as gr | |
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC, Wav2Vec2CTCTokenizer | |
# Load the Arabic-specific processor and model | |
model_name = "Zaid/wav2vec2-large-xlsr-53-arabic-egyptian" | |
tokenizer = Wav2Vec2CTCTokenizer.from_pretrained(model_name) | |
processor = Wav2Vec2Processor.from_pretrained(model_name, tokenizer=tokenizer) | |
model = Wav2Vec2ForCTC.from_pretrained(model_name) | |
def transcribe(audio_file): | |
try: | |
# Load the audio file | |
print("Loading audio file...") | |
audio_input, sr = torchaudio.load(audio_file) | |
print(f"Audio loaded: {audio_input.shape}, Sample rate: {sr}") | |
# Resample if needed | |
if sr != 16000: | |
print("Resampling audio...") | |
resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=16000) | |
audio_input = resampler(audio_input) | |
sr = 16000 | |
print(f"Audio shape after resampling: {audio_input.shape}, Sample rate: {sr}") | |
# Convert tensor to numpy array | |
audio_input = audio_input[0].numpy() | |
# Process audio input | |
print("Processing audio input...") | |
input_values = processor(audio_input, return_tensors="pt", sampling_rate=sr).input_values | |
# Run model inference | |
print("Running model inference...") | |
with torch.no_grad(): | |
logits = model(input_values).logits | |
# Decode transcription | |
print("Decoding transcription...") | |
predicted_ids = torch.argmax(logits, dim=-1) | |
transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True) | |
return transcription[0] | |
except Exception as e: | |
print(f"An error occurred: {e}") | |
return None | |
# Transcribe the audio file | |
transcription = transcribe("sidiali.wav") | |
if transcription: | |
print(transcription.encode('utf-8').decode('utf-8')) | |
# Save the transcription to a file | |
# with open("transcription.txt", "w", encoding="utf-8") as f: | |
# f.write(transcription) | |
# print("Transcription saved to transcription.txt") | |