first commit

app.py

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+import gradio as gr
+import torch
+from transformers import WhisperForConditionalGeneration, WhisperProcessor, WhisperTokenizer
+import librosa
+
+model_name = "shReYas0363/whisper-tiny-fine-tuned" 
+processor = WhisperProcessor.from_pretrained("openai/whisper-tiny")
+tokenizer=WhisperTokenizer.from_pretrained("openai/whisper-tiny")
+model = WhisperForConditionalGeneration.from_pretrained(model_name)
+
+def transcribe(audio_file_path):
+    audio, sr = librosa.load(audio_file_path, sr=16000)
+    input_features = processor(audio, return_tensors="pt", sampling_rate=16000).input_features
+    with torch.no_grad():
+        predicted_ids = model.generate(input_features)
+    transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)[0]
+    return transcription
+
+interface = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(sources=["upload","microphone"], type="filepath"),
+    outputs="Transcription",
+    title="Whisper Tiny ASR",
+    description="Fine-tuned on AI4Bharat's svarah dataset"
+)
+
+interface.launch()

requirements.txt

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+gradio
+torch
+transformers
+librosa