Create app.py

app.py

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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
+
+# Load the "clean" LibriSpeech dataset
+
+dataset = load_dataset("librispeech_asr", "clean", split="train")
+
+# 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.")
+