Create app.py

app.py

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+import torch
+import torchaudio
+import gradio as gr
+from transformers import AutoProcessor, AutoModelForSpeechSeq2Seq
+
+# device setup
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+# load model + processor
+model_name = "ibm-granite/granite-speech-3.3-8b"
+processor = AutoProcessor.from_pretrained(model_name)
+tokenizer = processor.tokenizer
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_name, device_map=device, torch_dtype=torch.bfloat16
+)
+
+today_str = date.today().strftime("%B %d, %Y")
+
+system_prompt = (
+    "Knowledge Cutoff Date: April 2024.\n"
+    f"Today's Date: {today_str}.\n"
+    "You are Granite, developed by IBM. You are a helpful AI assistant."
+)
+
+def transcribe(audio_file):
+    # load wav file
+    wav, sr = torchaudio.load(audio_file, normalize=True)
+    if wav.shape[0] != 1 or sr != 16000:
+        # resample + convert to mono if needed
+        wav = torch.mean(wav, dim=0, keepdim=True)  # mono
+        wav = torchaudio.functional.resample(wav, sr, 16000)
+        sr = 16000
+
+    # user prompt
+    user_prompt = "<|audio|>can you transcribe the speech into a written format?"
+    chat = [
+        dict(role="system", content=system_prompt),
+        dict(role="user", content=user_prompt),
+    ]
+    prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+
+    # run model
+    model_inputs = processor(prompt, wav, sampling_rate=sr, device=device, return_tensors="pt").to(device)
+    model_outputs = model.generate(
+        **model_inputs, 
+        max_new_tokens=200, 
+        do_sample=False, 
+        num_beams=1
+    )
+
+    # strip prompt tokens
+    num_input_tokens = model_inputs["input_ids"].shape[-1]
+    new_tokens = torch.unsqueeze(model_outputs[0, num_input_tokens:], dim=0)
+    output_text = tokenizer.batch_decode(
+        new_tokens, add_special_tokens=False, skip_special_tokens=True
+    )
+
+    return output_text[0].strip()
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## Granite 3.3 Speech-to-Text Demo")
+
+    with gr.Row():
+        audio_input = gr.Audio(type="filepath", label="Upload Audio (16kHz mono preferred)")
+        output_text = gr.Textbox(label="Transcription", lines=5)
+
+    transcribe_btn = gr.Button("Transcribe")
+    transcribe_btn.click(fn=transcribe, inputs=audio_input, outputs=output_text)
+
+    demo.launch()