app.py

import os
import tarfile
import torch
import torchaudio
import numpy as np
import streamlit as st
import matplotlib.pyplot as plt
from huggingface_hub import login
from datasets import load_dataset, DatasetDict
from transformers import (
    AutoProcessor,
    AutoModelForSpeechSeq2Seq,
    TrainingArguments,
    Trainer,
    DataCollatorForSeq2Seq,
)

# ================================
# 1️⃣ Authenticate with Hugging Face Hub (Securely)
# ================================
HF_TOKEN = os.getenv("hf_token")  

if HF_TOKEN is None:
    raise ValueError("❌ Hugging Face API token not found. Please set it in Secrets.")

login(token=HF_TOKEN)

# ================================
# 2️⃣ Load Model & Processor
# ================================
MODEL_NAME = "AqeelShafy7/AudioSangraha-Audio_to_Text"
processor = AutoProcessor.from_pretrained(MODEL_NAME)
model = AutoModelForSpeechSeq2Seq.from_pretrained(MODEL_NAME)

device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
print(f"✅ Model loaded on {device}")

# ================================
# 3️⃣ Load and Prepare Dataset
# ================================
DATASET_TAR_PATH = "dev-clean.tar.gz"
EXTRACT_PATH = "./librispeech_dev_clean"

if not os.path.exists(EXTRACT_PATH):
    print("🔄 Extracting dataset...")
    with tarfile.open(DATASET_TAR_PATH, "r:gz") as tar:
        tar.extractall(EXTRACT_PATH)
    print("✅ Extraction complete.")
else:
    print("✅ Dataset already extracted.")

# Load dataset with transcripts
dataset = load_dataset("librispeech_asr", "clean", split="train")

# Ensure dataset has transcripts
if "text" not in dataset.column_names:
    raise ValueError("❌ Dataset is missing transcription text!")

# Preprocessing Function
def preprocess_data(batch):
    # Process audio
    waveform, sample_rate = torchaudio.load(batch["file"])
    waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)
    
    batch["input_features"] = processor(waveform.squeeze().numpy(), sampling_rate=16000, return_tensors="pt").input_features[0]
    
    # Tokenize transcript text
    batch["labels"] = processor.tokenizer(batch["text"], padding="max_length", truncation=True, return_tensors="pt").input_ids[0]
    
    return batch

# Apply preprocessing
dataset = dataset.map(preprocess_data, remove_columns=["file", "audio", "text"])

# Split into train & eval
train_size = int(0.8 * len(dataset))
train_dataset = dataset.select(range(train_size))
eval_dataset = dataset.select(range(train_size, len(dataset)))

print(f"✅ Dataset Prepared! Training: {len(train_dataset)}, Evaluation: {len(eval_dataset)}")

# ================================
# 4️⃣ Training Arguments & Trainer
# ================================
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,
    hub_model_id="tahirsher/ASR_Model_for_Transcription_into_Text",
    hub_token=HF_TOKEN,
)

data_collator = DataCollatorForSeq2Seq(tokenizer=processor.tokenizer, model=model, return_tensors="pt")

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    data_collator=data_collator,
)

# ================================
# 5️⃣ Fine-Tuning Execution & Training Stats
# ================================
if st.button("Start Fine-Tuning"):
    with st.spinner("Fine-tuning in progress... Please wait!"):
        trainer.train()
    st.success("✅ Fine-Tuning Completed! Model updated.")

    # Plot Training Loss
    train_loss = trainer.state.log_history
    losses = [entry['loss'] for entry in train_loss if 'loss' in entry]
    
    plt.figure(figsize=(8, 5))
    plt.plot(range(len(losses)), losses, label="Training Loss", color="blue")
    plt.xlabel("Steps")
    plt.ylabel("Loss")
    plt.title("Training Loss Over Time")
    plt.legend()
    st.pyplot(plt)

# ================================
# 6️⃣ Streamlit ASR Web App (Proper Decoding)
# ================================
st.title("🎙️ Speech-to-Text ASR Model with Fine-Tuning 🎶")

audio_file = st.file_uploader("Upload an audio file", type=["wav", "mp3", "flac"])

if audio_file:
    audio_path = "temp_audio.wav"
    with open(audio_path, "wb") as f:
        f.write(audio_file.read())

    waveform, sample_rate = torchaudio.load(audio_path)
    waveform = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000)(waveform)

    input_features = processor(waveform.squeeze().numpy(), sampling_rate=16000, return_tensors="pt").input_features

    input_tensor = input_features.to(device)

    # ✅ FIX: Use `generate()` for Proper Transcription
    with torch.no_grad():
        generated_ids = model.generate(
            input_tensor,
            max_length=500,
            num_beams=5,
            do_sample=True,
            top_k=50
        )
        transcription = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]

    # Display transcription
    st.success("📄 Transcription:")
    st.write(transcription)