Add a standalone onnx demo

moonshine/demo/README.md

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+# Moonshine Demos
+
+This directory contains various scripts the demonstrate the capabilities of the Moonshine ASR models.
+
+## onnx_standalone.py
+
+This script demonstrates how to run a Moonshine model with the `onnxruntime` package alone, without depending on `torch` or `tensorflow`. This enables running on SBCs such as Raspberry Pi. Follow the instructions below to setup and run.
+
+* Install `onnxruntime` (or `onnxruntime-gpu` if you want to run on GPUs) and `tokenizers` packages using your Python package manager of choice, such as `pip`.
+
+* Download the `onnx` files from huggingface hub to a directory.
+
+  ```shell
+  mkdir moonshine_base_onnx
+  cd moonshine_base_onnx
+  wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/preprocess.onnx
+  wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/encode.onnx
+  wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/uncached_decode.onnx
+  wget https://huggingface.co/UsefulSensors/moonshine/resolve/main/onnx/base/cached_decode.onnx
+  cd ..
+  ```
+
+* Run `onnx_standalone.py` to transcribe a wav file
+
+  ```shell
+  moonshine/moonshine/demo/onnx_standalone.py --models_dir moonshine_base_onnx --wav_file moonshine/moonshine/assets/beckett.wav
+  ['Ever tried ever failed, no matter try again fail again fail better.']
+  ```

moonshine/demo/onnx_standalone.py

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+import os
+import sys
+import argparse
+import wave
+import numpy as np
+import tokenizers
+
+MOONSHINE_DEMO_DIR = os.path.dirname(__file__)
+sys.path.append(os.path.join(MOONSHINE_DEMO_DIR, ".."))
+
+from onnx_model import MoonshineOnnxModel
+
+
+def main(models_dir, wav_file):
+    m = MoonshineOnnxModel(models_dir=models_dir)
+    with wave.open(wav_file) as f:
+        params = f.getparams()
+        assert (
+            params.nchannels == 1
+            and params.framerate == 16_000
+            and params.sampwidth == 2
+        ), f"wave file should have 1 channel, 16KHz, and int16"
+        audio = f.readframes(params.nframes)
+    audio = np.frombuffer(audio, np.int16) / 32768.0
+    audio = audio.astype(np.float32)[None, ...]
+    tokens = m.generate(audio)
+    tokenizer = tokenizers.Tokenizer.from_file(
+        os.path.join(MOONSHINE_DEMO_DIR, "..", "assets", "tokenizer.json")
+    )
+    text = tokenizer.decode_batch(tokens)
+    print(text)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        prog="onnx_standalone",
+        description="Standalone ONNX demo of Moonshine models",
+    )
+    parser.add_argument(
+        "--models_dir", help="Directory containing ONNX files", required=True
+    )
+    parser.add_argument("--wav_file", help="Speech WAV file", required=True)
+    args = parser.parse_args()
+    main(**vars(args))