Add initial Gradio demo

pyproject.toml

@@ -5,6 +5,8 @@ description = "Huggingface Space to test NVIDIA Nemo's Parakeet v2 speech recogn
 readme = "README.md"
 requires-python = ">=3.10"
 dependencies = [
+    "gradio>=5.29.0",
     "nemo-toolkit[asr]>=2.2.1",
     "numpy<2.0",
+    "scipy>=1.15.2",
 ]

run.py

@@ -1,16 +1,117 @@
-import sys
-
+import gradio as gr
 import nemo.collections.asr as nemo_asr
+import numpy as np
+from scipy import signal
+
+TARGET_SR = 16_000  # Hz
+TITLE = "NVIDIA's Parakeet TDT 0.6B v2 Demo"
+DESCRIPTION = """## Description
+
+NVIDIA's parakeet-tdt-0.6b-v2 is a 600-million-parameter automatic \
+speech recognition (ASR) model designed for high-quality English \
+transcription, featuring support for punctuation, capitalization, \
+and accurate timestamp prediction. This is a state-of-the-art model \
+ideal for: accurate word-level timestamp predictions, automatic \
+punctuation and capitalization, robust performance on spoken numbers, \
+and song lyrics transcription.
+
+### License
+
+The license is comercial friendly:
+
+> GOVERNING TERMS: Use of this model is governed by the [CC-BY-4.0 license](https://creativecommons.org/licenses/by/4.0/deed.en).
+
+### Contact
+
+Need help adding transcription to your system? [Let's talk!](mailto:[email protected]).\
+At [RidgeRun.ai](https://ridgerun.ai) we'd love to help.
+
+### Links of Interest
+* [Model card](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2)
+* [NVIDIA NeMO](https://github.com/NVIDIA/NeMo)
+
+## Playground
+
+"""
+
+_model = None
+
+
+def _to_float32(x: np.ndarray) -> np.ndarray:
+    """
+    Convert any integer PCM array to float32 in the range [-1, 1].
+    Works for signed (int8/16/24/32) and unsigned (uint8) types,
+    without hard‑coded magic numbers.
+    """
+    if not np.issubdtype(x.dtype, np.integer):
+        # Already float – just ensure dtype is float32
+        return x.astype(np.float32, copy=False)
 
+    info = np.iinfo(x.dtype)
+    x = x.astype(np.float32)
 
-def main(argv):
-    model = nemo_asr.models.ASRModel.from_pretrained(
-        model_name="nvidia/parakeet-tdt-0.6b-v2"
+    # signed PCM (e.g. int16, int32)
+    if info.min < 0:
+        # ‑32768..32767 -> ‑1..1
+        x /= max(abs(info.min), info.max)
+
+    # unsigned PCM (e.g. uint8 0..255)
+    else:
+        # 128.0 for uint8
+        midpoint = (info.max + 1) / 2
+        # 0..255 -> ‑1..1
+        x = (x - midpoint) / midpoint
+
+    return x
+
+
+def _resample(audio: np.ndarray, rate: int, target_rate: int) -> np.ndarray:
+    if rate == target_rate:
+        return audio
+
+    # Use polyphase filtering for efficient, high‑quality resampling.
+    gcd = np.gcd(rate, target_rate)
+    up = target_rate // gcd
+    down = rate // gcd
+    resampled = signal.resample_poly(
+        _to_float32(audio), up=up, down=down, axis=0
     )
 
-    output = model.transcribe([argv[1]])
-    print(output[0].text)
+    return resampled
+
+
+def _invoke_model(audio: np.ndarray):
+    global _model
+    if not _model:
+        _model = nemo_asr.models.ASRModel.from_pretrained(
+            model_name="nvidia/parakeet-tdt-0.6b-v2"
+        )
+
+    return _model.transcribe(audio=audio)[0].text
+
+
+def transcribe(audio: tuple[np.ndarray, int] | None):
+    if not audio:
+        return "No audio received. Please upload or record something"
+
+    rate, data = audio
+
+    data = _to_float32(data)
+    data = _resample(data, rate, TARGET_SR)
+    return _invoke_model(data)
+
 
+app = gr.Interface(
+    fn=transcribe,
+    inputs=gr.Audio(
+        sources=["upload", "microphone"],
+        type="numpy",
+        label="Upload or record audio",
+    ),
+    outputs=gr.Textbox(label="Transcription", show_copy_button=True),
+    title=TITLE,
+    description=DESCRIPTION,
+)
 
 if __name__ == "__main__":
-    main(sys.argv)
+    app.launch()

uv.lock

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 name = "packaging"
 version = "24.2"
@@ -2474,14 +2691,18 @@ name = "parakeet-tdt-0-6b-v2"
 version = "0.1.0"
 source = { virtual = "." }
 dependencies = [
+    { name = "gradio" },
     { name = "nemo-toolkit", extra = ["asr"] },
     { name = "numpy" },
+    { name = "scipy" },
 ]
 
 [package.metadata]
 requires-dist = [
+    { name = "gradio", specifier = ">=5.29.0" },
     { name = "nemo-toolkit", extras = ["asr"], specifier = ">=2.2.1" },
     { name = "numpy", specifier = "<2.0" },
+    { name = "scipy", specifier = ">=1.15.2" },
 ]
 
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