Add app.py

app.py

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+"""Japanese ASR demo for sbintuitions/kana-whisper on Hugging Face ZeroGPU.
+
+The model is Whisper large-v3-turbo fine-tuned to transcribe Japanese speech
+into *katakana* (pronunciation), e.g. キョーワイイテンキデスネ. It was built as
+the ASR component for evaluating the reading accuracy of Japanese TTS systems.
+"""
+
+import numpy as np
+import spaces
+import torch
+import gradio as gr
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
+MODEL_ID = "sbintuitions/kana-whisper"
+
+# ZeroGPU exposes CUDA at startup, so we can load straight onto the GPU. The
+# @spaces.GPU decorator on transcribe() is what actually reserves the slice at
+# call time; on non-ZeroGPU hardware the decorator is a no-op.
+device = "cuda"
+torch_dtype = torch.float16
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    MODEL_ID,
+    torch_dtype=torch_dtype,
+    low_cpu_mem_usage=True,
+    use_safetensors=True,
+).to(device)
+
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+
+asr = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+GENERATE_KWARGS = {"language": "ja", "task": "transcribe"}
+
+
+def _to_float_mono(sampling_rate: int, data: np.ndarray) -> dict:
+    """Normalize Gradio's (sr, ndarray) audio into what the ASR pipeline wants:
+    a float32 mono waveform in [-1, 1] with its sampling rate (the feature
+    extractor resamples to 16 kHz internally)."""
+    data = np.asarray(data)
+    if data.ndim > 1:  # stereo -> mono
+        data = data.mean(axis=1)
+    if np.issubdtype(data.dtype, np.integer):
+        data = data.astype(np.float32) / np.iinfo(data.dtype).max
+    else:
+        data = data.astype(np.float32)
+    return {"array": data, "sampling_rate": int(sampling_rate)}
+
+
+@spaces.GPU
+def transcribe(audio: tuple | None) -> str:
+    if audio is None:
+        return ""
+    sampling_rate, data = audio
+    inputs = _to_float_mono(sampling_rate, data)
+    result = asr(inputs, generate_kwargs=GENERATE_KWARGS)
+    return result["text"]
+
+
+DESCRIPTION = """
+# 🎙️ Kana Whisper — Japanese ASR → Katakana
+
+Record or upload a short Japanese audio clip and get back its **katakana**
+transcription (pronunciation, not kanji) using
+[`sbintuitions/kana-whisper`](https://huggingface.co/sbintuitions/kana-whisper)
+— a Whisper large-v3-turbo model fine-tuned by SB Intuitions.
+
+> Example output: `キョーワイイテンキデスネ`
+"""
+
+with gr.Blocks(title="Kana Whisper — Japanese ASR") as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Row():
+        audio_in = gr.Audio(
+            sources=["microphone", "upload"],
+            type="numpy",
+            label="Japanese audio (short clip)",
+        )
+        text_out = gr.Textbox(
+            label="Katakana transcription",
+            lines=4,
+            show_copy_button=True,
+        )
+    transcribe_btn = gr.Button("Transcribe", variant="primary")
+    transcribe_btn.click(fn=transcribe, inputs=audio_in, outputs=text_out)
+
+if __name__ == "__main__":
+    demo.launch()