RECCoon 1.0.0: rebrand to Tom Cooks, Parakeet-only, GPLv3, F-Droid-ready

16 files changed, 721 insertions, 0 deletions

diff --git a/tools/whistle-eval/.gitignore b/tools/whistle-eval/.gitignore
new file mode 100644
index 0000000..f28fcce
--- /dev/null
+++ b/tools/whistle-eval/.gitignore
@@ -0,0 +1,7 @@
+node_modules/
+vendor/
+models/
+.venv/
+__pycache__/
+samples/*.wav
+samples/*.mp3
diff --git a/tools/whistle-eval/README.md b/tools/whistle-eval/README.md
new file mode 100644
index 0000000..ed6e71e
--- /dev/null
+++ b/tools/whistle-eval/README.md
@@ -0,0 +1,94 @@
+# Speech-model evaluation
+
+Compares on-device speech recognition candidates on the same 16 kHz mono clips:
+
+- the multilingual Whisper tiny/base models that RECCoon already uses,
+  through the exact sherpa-onnx 1.13.8 version the Android app bundles;
+- [NVIDIA Parakeet TDT 0.6B v3](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v3)
+  (25 European languages) via its sherpa-onnx int8 export;
+- [Cactus Whistle](https://huggingface.co/mrfakename/whistle-ONNX), a custom
+  *Needle* encoder/decoder run through its reference Node pipeline.
+
+Whistle is **not** a Whisper model and cannot be loaded with sherpa-onnx.
+Parakeet, unlike Whistle, **is** supported by sherpa-onnx.
+
+## Setup
+
+Requires Node 18+, Python 3, `ffmpeg`, and `curl`.
+
+```sh
+cd tools/whistle-eval
+
+# 1. Whistle pack + ONNX graphs + reference JS + onnxruntime-node
+#    and the Whisper tiny/base int8 models.
+./setup.sh
+
+# 2. Parakeet v3 int8 (~660 MB, optional)
+mkdir -p models/parakeet-v3-int8
+BASE=https://huggingface.co/csukuangfj/sherpa-onnx-nemo-parakeet-tdt-0.6b-v3-int8/resolve/main
+for f in encoder.int8.onnx decoder.int8.onnx joiner.int8.onnx tokens.txt; do
+  curl -L --fail -o "models/parakeet-v3-int8/$f" "$BASE/$f"
+done
+
+# 3. Python environment for the sherpa-onnx baselines.
+python3 -m venv .venv
+.venv/bin/pip install sherpa-onnx numpy
+```
+
+`setup.sh` downloads the 17 MB `whistle.pack` and rebuilds the fp32
+`encoder.onnx.data` / `decoder.onnx.data` files locally with `build-data.mjs`.
+They are **not** committed (see `.gitignore`); the pack is enough to recreate
+them byte-exactly.
+
+## Run
+
+```sh
+# One clip (language inferred: `it*` -> Italian, otherwise English)
+.venv/bin/python evaluate.py samples/jfk.wav
+
+# A whole folder
+.venv/bin/python evaluate.py "samples/*.wav"
+```
+
+`evaluate.py` prints the transcript and the word error rate (WER) against
+`samples/<name>.txt` when a reference exists. Parakeet is included
+automatically when `models/parakeet-v3-int8/` is present.
+
+| clip | Whisper tiny | Whisper base | Whistle | **Parakeet v3** |
+|---|---|---|---|---|
+| `jfk.wav` | 0% | 4.5% | 0% | 0% |
+| `en1.wav` | 0% | 0% | 8.7% | 0% |
+| `en2.wav` | 13.6% | 13.6% | 13.6% | 13.6% |
+| `it1.wav` | 24% | 12% | 20% | **4%** |
+| `it1_noisy.wav` | 24% | 12% | 24% | **0%** |
+| `it2.wav` | 21.7% | 26.1% | 4.3% | 4.3% |
+
+Average WER over these six clips (lower is better):
+
+| model | English (3) | Italian (3) | All (6) | int8 size |
+|---|---|---|---|---|
+| Whisper tiny | 4.5% | 23.2% | 13.9% | ~103 MB |
+| Whisper base | 6.0% | 16.7% | 11.4% | ~160 MB |
+| Whistle | 7.4% | 16.1% | 11.8% | ~17 MB pack (desktop) |
+| **Parakeet v3** | 4.5% | **2.8%** | **3.7%** | ~670 MB |
+
+### Reading the result
+
+- **Parakeet v3 is the clear winner**, especially for Italian and noisy audio:
+  it was perfect on the noisy Italian clip that fooled every other model.
+- The only Parakeet "errors" are number formatting ("four fifteen" → "4:15"),
+  which WER counts even though the transcript is correct.
+- The cost is size: ~670 MB int8, about four times Whisper base. It is usable
+  on a modern phone but is a large download and a lot of RAM.
+- Whisper base stays the best small model; Whistle is competitive but not a
+  clear win, and needs a custom runtime.
+- These are small neural-TTS clips, not real recordings. Run the harness on
+  real RECCoon recordings before tuning further.
+
+## Android notes
+
+- Parakeet can be added through the existing sherpa-onnx AAR with
+  `model_type="nemo_transducer"` (Java: `OfflineTransducerModelConfig`). RECCoon
+  1.5.0-alpha exposes it as the "Parakeet v3 (best)" model in the player.
+- Whistle would need `onnxruntime-android` plus the log-mel frontend, BPE
+  tokenizer, engram features and greedy decoder from `vendor/js/whistle.js`.
diff --git a/tools/whistle-eval/build-data.mjs b/tools/whistle-eval/build-data.mjs
new file mode 100644
index 0000000..20be062
--- /dev/null
+++ b/tools/whistle-eval/build-data.mjs
@@ -0,0 +1,19 @@
+// Rebuilds the fp32 ONNX external-data files from the compact whistle.pack.
+// This is the "small weights" path: instead of committing ~145 MB of .onnx.data
+// to git (or downloading it), keep the 17 MB pack and expand it on first run.
+import fs from 'fs';
+import { parsePack, buildModelData } from './vendor/js/pack.js';
+
+const root = new URL('./vendor/', import.meta.url);
+const packBytes = fs.readFileSync(new URL('whistle.pack', root));
+const pack = parsePack(
+  packBytes.buffer.slice(packBytes.byteOffset, packBytes.byteOffset + packBytes.byteLength));
+
+for (const name of ['encoder', 'decoder']) {
+  const target = new URL(`onnx/${name}.onnx.data`, root);
+  const data = buildModelData(pack, name, (p) => {
+    process.stdout.write(`\r${name}: ${(p * 100).toFixed(0)}%   `);
+  });
+  fs.writeFileSync(target, data);
+  process.stdout.write(`\r${name}: wrote ${data.length} bytes\n`);
+}
diff --git a/tools/whistle-eval/evaluate.py b/tools/whistle-eval/evaluate.py
new file mode 100644
index 0000000..f4437ea
--- /dev/null
+++ b/tools/whistle-eval/evaluate.py
@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+"""Compare Cactus Whistle with the app's Whisper models on the same clips.
+
+Usage:
+    python evaluate.py samples/jfk.wav en
+    python evaluate.py samples/*.wav        # language guessed from a *.it.wav suffix
+
+For every clip this runs:
+  * Whisper tiny (int8, multilingual) through sherpa-onnx
+  * Whisper base (int8, multilingual) through sherpa-onnx
+  * Cactus Whistle through its reference Node pipeline
+and prints the transcript plus the word error rate (WER) against
+`samples/<name>.txt` when that reference file exists.
+
+Setup (see README.md):
+    npm install
+    node build-data.mjs
+    python3 -m venv .venv && .venv/bin/pip install sherpa-onnx numpy
+"""
+import argparse
+import glob
+import os
+import re
+import subprocess
+import sys
+import unicodedata
+import wave
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+VENV = os.environ.get("WHISTLE_EVAL_PYTHON", sys.executable)
+
+
+def normalize(text: str) -> list:
+    text = unicodedata.normalize("NFKD", text.lower())
+    text = "".join(c for c in text if not unicodedata.combining(c))
+    text = re.sub(r"[^a-z0-9]+", " ", text)
+    return text.split()
+
+
+def wer(reference: list, hypothesis: list) -> float:
+    if not reference:
+        return 0.0 if not hypothesis else 1.0
+    previous = list(range(len(hypothesis) + 1))
+    for i, ref in enumerate(reference, start=1):
+        current = [i]
+        for j, hyp in enumerate(hypothesis, start=1):
+            cost = 0 if ref == hyp else 1
+            current.append(min(previous[j] + 1, current[j - 1] + 1, previous[j - 1] + cost))
+        previous = current
+    return previous[-1] / len(reference)
+
+
+def read_reference(audio: str):
+    base = os.path.splitext(audio)[0]
+    for candidate in (base + ".txt", audio + ".txt"):
+        if os.path.isfile(candidate):
+            with open(candidate, encoding="utf-8") as handle:
+                return handle.read().strip()
+    return None
+
+
+def run_whistle(audio: str) -> str:
+    out = subprocess.run(
+        ["node", os.path.join(HERE, "vendor", "js", "example.mjs"), audio],
+        cwd=HERE, capture_output=True, text=True, timeout=600)
+    if out.returncode != 0:
+        return f"<whistle failed: {out.stderr.strip().splitlines()[-1] if out.stderr else '?'}>"
+    first = out.stdout.strip().splitlines()[0] if out.stdout.strip() else ""
+    return re.sub(r"^\[[a-z]{2}\]\s*", "", first)
+
+
+def parakeet_available() -> bool:
+    return os.path.isfile(os.path.join(
+        HERE, "models", "parakeet-v3-int8", "encoder.int8.onnx"))
+
+
+def run_parakeet(audio: str) -> str:
+    script = os.path.join(HERE, "parakeet_baseline.py")
+    out = subprocess.run(
+        [VENV, script, audio], capture_output=True, text=True, timeout=1200)
+    if out.returncode != 0:
+        tail = out.stderr.strip().splitlines()
+        return f"<parakeet failed: {tail[-1] if tail else '?'}>"
+    return out.stdout.strip()
+
+
+def run_whisper(audio: str, size: str, language: str) -> str:
+    script = os.path.join(HERE, "whisper_baseline.py")
+    out = subprocess.run(
+        [VENV, script, audio, size, language],
+        capture_output=True, text=True, timeout=900)
+    if out.returncode != 0:
+        tail = out.stderr.strip().splitlines()
+        return f"<whisper {size} failed: {tail[-1] if tail else '?'}>"
+    return out.stdout.strip()
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("clips", nargs="+")
+    parser.add_argument("language", nargs="?", default=None)
+    args = parser.parse_args()
+
+    clips = []
+    for pattern in args.clips:
+        clips.extend(sorted(glob.glob(pattern)) or [pattern])
+
+    print(f"{'clip':<16} {'model':<14} {'WER':>7}  transcript")
+    print("-" * 100)
+    for audio in clips:
+        if not os.path.isfile(audio):
+            print(f"{audio}: not found")
+            continue
+        language = args.language
+        name = os.path.basename(audio).lower()
+        if language is None:
+            language = "it" if (name.startswith("it") or ".it." in name) else "en"
+        reference = read_reference(audio)
+        ref_words = normalize(reference) if reference else None
+
+        results = [
+            ("whistle", run_whistle(audio)),
+            ("whisper tiny", run_whisper(audio, "tiny", language)),
+            ("whisper base", run_whisper(audio, "base", language)),
+        ]
+        if parakeet_available():
+            results.append(("parakeet v3", run_parakeet(audio)))
+        for model, text in results:
+            if ref_words is None:
+                score = "   n/a"
+            else:
+                score = f"{wer(ref_words, normalize(text)) * 100:6.1f}%"
+            print(f"{os.path.basename(audio):<16} {model:<14} {score}  {text}")
+        if reference:
+            print(f"{'':<16} {'reference':<14} {'':>7}  {reference}")
+        print()
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/tools/whistle-eval/package-lock.json b/tools/whistle-eval/package-lock.json
new file mode 100644
index 0000000..063b6cc
--- /dev/null
+++ b/tools/whistle-eval/package-lock.json
@@ -0,0 +1,229 @@
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+      "name": "reccoon-whistle-eval",
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+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/sponsors/sindresorhus"
+      }
+    }
+  }
+}
diff --git a/tools/whistle-eval/package.json b/tools/whistle-eval/package.json
new file mode 100644
index 0000000..b34444b
--- /dev/null
+++ b/tools/whistle-eval/package.json
@@ -0,0 +1,14 @@
+{
+  "name": "reccoon-whistle-eval",
+  "version": "1.0.0",
+  "private": true,
+  "type": "module",
+  "description": "Desktop evaluation harness for the Cactus Whistle ASR model.",
+  "scripts": {
+    "build-data": "node build-data.mjs",
+    "transcribe": "node vendor/js/example.mjs"
+  },
+  "dependencies": {
+    "onnxruntime-node": "^1.20.1"
+  }
+}
diff --git a/tools/whistle-eval/parakeet_baseline.py b/tools/whistle-eval/parakeet_baseline.py
new file mode 100644
index 0000000..16987de
--- /dev/null
+++ b/tools/whistle-eval/parakeet_baseline.py
@@ -0,0 +1,67 @@
+#!/usr/bin/env python3
+"""Run NVIDIA Parakeet TDT 0.6B v3 (multilingual) through sherpa-onnx.
+
+Usage:
+    python parakeet_baseline.py <audio.wav>
+
+Requires a virtualenv with `sherpa-onnx` installed and the int8 model under
+./models/parakeet-v3-int8/ (see README.md).
+"""
+import argparse
+import os
+import sys
+import wave
+
+import numpy as np
+import sherpa_onnx
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("audio")
+    args = parser.parse_args()
+
+    model_dir = os.path.join(os.path.dirname(__file__), "models", "parakeet-v3-int8")
+    encoder = os.path.join(model_dir, "encoder.int8.onnx")
+    decoder = os.path.join(model_dir, "decoder.int8.onnx")
+    joiner = os.path.join(model_dir, "joiner.int8.onnx")
+    tokens = os.path.join(model_dir, "tokens.txt")
+    for path in (encoder, decoder, joiner, tokens):
+        if not os.path.isfile(path):
+            print(f"missing {path}", file=sys.stderr)
+            return 2
+
+    recognizer = sherpa_onnx.OfflineRecognizer.from_transducer(
+        encoder=encoder,
+        decoder=decoder,
+        joiner=joiner,
+        tokens=tokens,
+        num_threads=2,
+        sample_rate=16000,
+        feature_dim=80,
+        decoding_method="greedy_search",
+        model_type="nemo_transducer",
+        debug=False,
+    )
+
+    with wave.open(args.audio, "rb") as wav:
+        sample_rate = wav.getframerate()
+        channels = wav.getnchannels()
+        width = wav.getsampwidth()
+        frames = wav.readframes(wav.getnframes())
+    if width != 2:
+        print("only 16-bit PCM WAV is supported", file=sys.stderr)
+        return 2
+    samples = np.frombuffer(frames, dtype=np.int16).astype(np.float32) / 32768.0
+    if channels > 1:
+        samples = samples.reshape(-1, channels).mean(axis=1)
+
+    stream = recognizer.create_stream()
+    stream.accept_waveform(sample_rate, samples)
+    recognizer.decode_stream(stream)
+    print(stream.result.text)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/tools/whistle-eval/samples/README.md b/tools/whistle-eval/samples/README.md
new file mode 100644
index 0000000..28f0d12
--- /dev/null
+++ b/tools/whistle-eval/samples/README.md
@@ -0,0 +1,41 @@
+# Test clips
+
+The audio files are not committed. Recreate them with `edge-tts` (neural TTS)
+and `ffmpeg`, or drop your own 16 kHz mono 16-bit WAV files here. When a
+`<name>.txt` sidecar exists, `evaluate.py` reports the WER against it.
+
+```sh
+# English
+edge-tts --voice en-US-AriaNeural \
+  --text "The quick brown fox jumps over the lazy dog near the riverbank, while the scientists carefully measured the temperature of the experimental apparatus." \
+  --write-media en1.mp3
+# Italian
+edge-tts --voice it-IT-ElsaNeural \
+  --text "Il rapido volpe marrone salta sopra il cane pigro vicino alla riva del fiume, mentre gli scienziati misurano con attenzione la temperatura dell'apparecchiatura sperimentale." \
+  --write-media it1.mp3
+# ...then:
+for f in en1 it1; do
+  ffmpeg -y -i "$f.mp3" -ar 16000 -ac 1 -c:a pcm_s16le "$f.wav"
+done
+```
+
+The JFK clip is the public sample from
+[whisper.cpp](https://github.com/ggerganov/whisper.cpp/blob/master/samples/jfk.wav).
+
+## References used by the committed test set
+
+| file | reference |
+|---|---|
+| `jfk.wav` | And so my fellow Americans ask not what your country can do for you ask what you can do for your country |
+| `en1.wav` | The quick brown fox jumps over the lazy dog near the riverbank, while the scientists carefully measured the temperature of the experimental apparatus. |
+| `en2.wav` | Please call me back at four fifteen in the afternoon. The meeting is with Doctor Smith and Professor Johnson from the university. |
+| `it1.wav` | Il rapido volpe marrone salta sopra il cane pigro vicino alla riva del fiume, mentre gli scienziati misurano con attenzione la temperatura dell'apparecchiatura sperimentale. |
+| `it2.wav` | Per favore, richiamami alle quattro e un quarto del pomeriggio. L'incontro e con il dottor Rossi e la professoressa Bianchi dell'universita. |
+
+`it1_noisy.wav` is `it1.wav` mixed with low-level white noise:
+
+```sh
+ffmpeg -y -i it1.wav -f lavfi -i "anoisesrc=color=white:amplitude=0.03:duration=999" \
+  -filter_complex "[0:a][1:a]amix=inputs=2:duration=first:weights=1 0.35" \
+  -ar 16000 -ac 1 -c:a pcm_s16le it1_noisy.wav
+```
diff --git a/tools/whistle-eval/samples/en1.txt b/tools/whistle-eval/samples/en1.txt
new file mode 100644
index 0000000..2848fe3
--- /dev/null
+++ b/tools/whistle-eval/samples/en1.txt
@@ -0,0 +1 @@
+The quick brown fox jumps over the lazy dog near the riverbank, while the scientists carefully measured the temperature of the experimental apparatus.
diff --git a/tools/whistle-eval/samples/en2.txt b/tools/whistle-eval/samples/en2.txt
new file mode 100644
index 0000000..da8a6ff
--- /dev/null
+++ b/tools/whistle-eval/samples/en2.txt
@@ -0,0 +1 @@
+Please call me back at four fifteen in the afternoon. The meeting is with Doctor Smith and Professor Johnson from the university.
diff --git a/tools/whistle-eval/samples/it1.txt b/tools/whistle-eval/samples/it1.txt
new file mode 100644
index 0000000..680e8da
--- /dev/null
+++ b/tools/whistle-eval/samples/it1.txt
@@ -0,0 +1 @@
+Il rapido volpe marrone salta sopra il cane pigro vicino alla riva del fiume, mentre gli scienziati misurano con attenzione la temperatura dell'apparecchiatura sperimentale.
diff --git a/tools/whistle-eval/samples/it1_noisy.txt b/tools/whistle-eval/samples/it1_noisy.txt
new file mode 100644
index 0000000..680e8da
--- /dev/null
+++ b/tools/whistle-eval/samples/it1_noisy.txt
@@ -0,0 +1 @@
+Il rapido volpe marrone salta sopra il cane pigro vicino alla riva del fiume, mentre gli scienziati misurano con attenzione la temperatura dell'apparecchiatura sperimentale.
diff --git a/tools/whistle-eval/samples/it2.txt b/tools/whistle-eval/samples/it2.txt
new file mode 100644
index 0000000..903d626
--- /dev/null
+++ b/tools/whistle-eval/samples/it2.txt
@@ -0,0 +1 @@
+Per favore, richiamami alle quattro e un quarto del pomeriggio. L'incontro e con il dottor Rossi e la professoressa Bianchi dell'universita.
diff --git a/tools/whistle-eval/samples/jfk.txt b/tools/whistle-eval/samples/jfk.txt
new file mode 100644
index 0000000..a67a12c
--- /dev/null
+++ b/tools/whistle-eval/samples/jfk.txt
@@ -0,0 +1 @@
+And so my fellow Americans ask not what your country can do for you ask what you can do for your country
diff --git a/tools/whistle-eval/setup.sh b/tools/whistle-eval/setup.sh
new file mode 100755
index 0000000..fde74fe
--- /dev/null
+++ b/tools/whistle-eval/setup.sh
@@ -0,0 +1,38 @@
+#!/usr/bin/env bash
+# Fetches everything the Whistle evaluation harness needs and rebuilds the
+# ONNX external-data files from the small whistle.pack.
+set -euo pipefail
+cd "$(dirname "$0")"
+
+WHISTLE_BASE="https://huggingface.co/mrfakename/whistle-ONNX/resolve/main"
+WHISPER_BASE="https://huggingface.co/csukuangfj/sherpa-onnx-whisper"
+
+mkdir -p vendor/js vendor/onnx models/tiny models/base
+
+echo "==> Downloading the Whistle pack and ONNX graphs"
+curl -L --fail -o vendor/whistle.pack "$WHISTLE_BASE/whistle.pack"
+curl -L --fail -o vendor/onnx/encoder.onnx "$WHISTLE_BASE/onnx/encoder.onnx"
+curl -L --fail -o vendor/onnx/decoder.onnx "$WHISTLE_BASE/onnx/decoder.onnx"
+
+echo "==> Downloading the reference JS pipeline"
+for file in pack.js whistle.js backend.js example.mjs; do
+  curl -L --fail -o "vendor/js/$file" "$WHISTLE_BASE/js/$file"
+done
+
+echo "==> Installing onnxruntime-node"
+npm install --no-audit --no-fund
+
+echo "==> Rebuilding fp32 ONNX data from whistle.pack"
+node build-data.mjs
+
+echo "==> Downloading Whisper tiny/base int8 (sherpa-onnx)"
+for model in tiny base; do
+  for file in encoder.int8.onnx decoder.int8.onnx tokens.txt; do
+    curl -L --fail -o "models/$model/$file" "$WHISPER_BASE-$model/resolve/main/$model-$file"
+  done
+done
+
+echo
+echo "Done. Next:"
+echo "  python3 -m venv .venv && .venv/bin/pip install sherpa-onnx numpy"
+echo "  .venv/bin/python evaluate.py \"samples/*.wav\""
diff --git a/tools/whistle-eval/whisper_baseline.py b/tools/whistle-eval/whisper_baseline.py
new file mode 100644
index 0000000..9be7014
--- /dev/null
+++ b/tools/whistle-eval/whisper_baseline.py
@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+"""Run the same sherpa-onnx multilingual Whisper model that the app uses.
+
+Usage:
+    python whisper_baseline.py <audio.wav> [tiny|base] [language]
+
+Requires a virtualenv with `sherpa-onnx` installed (see README.md). The model
+files are expected under ./models/<size>/.
+"""
+import argparse
+import os
+import sys
+import wave
+
+import numpy as np
+import sherpa_onnx
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("audio")
+    parser.add_argument("size", nargs="?", default="base", choices=["tiny", "base"])
+    parser.add_argument("language", nargs="?", default="en")
+    args = parser.parse_args()
+
+    model_dir = os.path.join(os.path.dirname(__file__), "models", args.size)
+    encoder = os.path.join(model_dir, "encoder.int8.onnx")
+    decoder = os.path.join(model_dir, "decoder.int8.onnx")
+    tokens = os.path.join(model_dir, "tokens.txt")
+    for path in (encoder, decoder, tokens):
+        if not os.path.isfile(path):
+            print(f"missing {path}", file=sys.stderr)
+            return 2
+
+    recognizer = sherpa_onnx.OfflineRecognizer.from_whisper(
+        encoder=encoder,
+        decoder=decoder,
+        tokens=tokens,
+        language=args.language,
+        task="transcribe",
+        num_threads=2,
+        decoding_method="greedy_search",
+        debug=False,
+    )
+    with wave.open(args.audio, "rb") as wav:
+        sample_rate = wav.getframerate()
+        channels = wav.getnchannels()
+        width = wav.getsampwidth()
+        frames = wav.readframes(wav.getnframes())
+    if width != 2:
+        print("only 16-bit PCM WAV is supported", file=sys.stderr)
+        return 2
+    samples = np.frombuffer(frames, dtype=np.int16).astype(np.float32) / 32768.0
+    if channels > 1:
+        samples = samples.reshape(-1, channels).mean(axis=1)
+
+    stream = recognizer.create_stream()
+    stream.accept_waveform(sample_rate, samples)
+    recognizer.decode_stream(stream)
+    print(stream.result.text)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())