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app.py

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+import gradio as gr
+import numpy as np
+import torch
+import torchaudio
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq, WhisperTokenizer
+
+# Define paths to the model and processor
+model_name = "userdata/whisper-largeV2-03-ms-v11-LORA-Merged"
+
+# Load the processor and model
+processor = AutoProcessor.from_pretrained(model_name)
+tokenizer = WhisperTokenizer.from_pretrained(model_name)
+model = AutoModelForSpeechSeq2Seq.from_pretrained(model_name)
+
+# Check and set the device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Function to chunk the audio
+def chunk_audio(audio, chunk_length):
+    num_chunks = len(audio) // chunk_length + (1 if len(audio) % chunk_length > 0 else 0)
+    return [audio[i * chunk_length:(i + 1) * chunk_length] for i in range(num_chunks)]
+
+# Function to transcribe an audio file
+def transcribe(audio_path, chunk_length=16000 * 30):  # 30 seconds chunks
+    # Load audio
+    speech_array, sampling_rate = torchaudio.load(audio_path)
+    # Resample to 16kHz
+    resampler = torchaudio.transforms.Resample(sampling_rate, 16000)
+    speech = resampler(speech_array).squeeze().numpy()
+
+    # Chunk the audio if it's too long
+    chunks = chunk_audio(speech, chunk_length)
+
+    # Transcribe each chunk
+    transcriptions = []
+    for chunk in chunks:
+        # Process the audio
+        inputs = processor(chunk, sampling_rate=16000, return_tensors="pt")
+        inputs = {key: value.to(device).to(torch.float16) for key, value in inputs.items()}  # Convert to float16
+
+        # Generate token IDs
+        with torch.no_grad():
+            generated_ids = model.generate(inputs["input_features"], max_length=448)
+
+        # Decode the token IDs to text
+        transcription = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+        transcriptions.append(transcription)
+
+    # Combine transcriptions
+    full_transcription = ' '.join(transcriptions)
+    return full_transcription
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=transcribe,  # Update to match the function name
+    inputs=gr.Audio(type="filepath"),
+    outputs=gr.Textbox(),
+    title="Audio Transcription App",
+    description="Upload an audio file to get a transcription."
+)
+
+# Launch the Gradio interface
+iface.launch()

requirements.txt

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+transformers==4.41.2
+gradio
+torch
+torchaudio