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from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
# Load Processor & Model
processor = AutoProcessor.from_pretrained("AqeelShafy7/AudioSangraha-Audio_to_Text")
model = AutoModelForSpeechSeq2Seq.from_pretrained("AqeelShafy7/AudioSangraha-Audio_to_Text")
# Move model to GPU if available
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
print(f"Model loaded on {device}")
from datasets import load_dataset
import torchaudio
import torch
# Fix: Add trust_remote_code=True
import fsspec
# Set a higher timeout limit
fsspec.config.conf["timeout"] = 20000 # minutes
dataset = load_dataset("librispeech_asr", "clean", split="train", trust_remote_code=True)
# Function to load & resample audio
def preprocess_audio(batch):
audio = batch["audio"]
waveform, sample_rate = torchaudio.load(audio["path"])
# Resample to 16kHz (ASR models usually require this)
waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
# Convert to correct format
batch["input_values"] = processor(waveform.squeeze().numpy(), sampling_rate=16000).input_values[0]
batch["labels"] = processor.tokenizer(batch["text"]).input_ids
return batch
# Apply preprocessing
dataset = dataset.map(preprocess_audio, remove_columns=["audio"])
from transformers import TrainingArguments, Trainer, DataCollatorForSeq2Seq
# Define Training Arguments
training_args = TrainingArguments(
output_dir="./asr_model_finetuned",
evaluation_strategy="epoch",
save_strategy="epoch",
learning_rate=5e-5,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=3,
weight_decay=0.01,
logging_dir="./logs",
logging_steps=500,
save_total_limit=2,
push_to_hub=True, # Enable uploading to Hugging Face Hub
)
# Define Data Collator
data_collator = DataCollatorForSeq2Seq(processor.tokenizer, model=model)
# Define Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=dataset,
eval_dataset=None, # We use only training data here
tokenizer=processor.feature_extractor,
data_collator=data_collator,
)
# Start Fine-Tuning
trainer.train()
# Deployment of Huggingface using streamlit
import streamlit as st
import soundfile as sf
import numpy as np
st.title("🎙️ Automatic Speech Recognition with Fine-Tuning 🎶")
# Upload audio file
audio_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "flac"])
if audio_file:
# Save and load audio file
with open("temp_audio.wav", "wb") as f:
f.write(audio_file.read())
waveform, sample_rate = torchaudio.load("temp_audio.wav")
# Resample to 16kHz
waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
# Convert to model input
input_values = processor(waveform.squeeze().numpy(), sampling_rate=16000).input_values[0]
# Perform transcription
with torch.no_grad():
input_tensor = torch.tensor([input_values]).to(device)
logits = model(input_tensor).logits
predicted_ids = torch.argmax(logits, dim=-1)
transcription = processor.batch_decode(predicted_ids)[0]
# Display transcription
st.success("Transcription:")
st.write(transcription)
# Fine-tune with user input
user_correction = st.text_area("Correct the transcription (if needed):")
if st.button("Fine-Tune Model"):
if user_correction:
# Convert correction to training format
corrected_input = processor.tokenizer(user_correction).input_ids
# Update dataset dynamically (simple approach)
dataset = dataset.add_item({"input_values": input_values, "labels": corrected_input})
# Retrain for one step
trainer.train()
st.success("Model fine-tuned successfully! Try another audio file.")
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