taco/microWakeWord-Trainer-Nvidia-Docker
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Release NVIDIA WakeWord Trainer v17

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This commit is contained in:
MasterPhooey
2026-07-25 17:23:31 -05:00
parent 426e4ec83f
commit 2eee70cb34
5 changed files with 42 additions and 7 deletions
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8
README.md
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@@ -22,7 +22,7 @@ docker pull ghcr.io/tatertotterson/microwakeword:latest
Tagged releases also publish matching immutable image tags:
```bash
docker pull ghcr.io/tatertotterson/microwakeword:v16
docker pull ghcr.io/tatertotterson/microwakeword:v17
```
The release tag must match `VERSION`. Update `WHATS_NEW.md` before tagging; the Docker workflow prepends it to GitHub's automatically generated release notes.
@@ -32,7 +32,7 @@ Python 3.13 TensorFlow build for `sm_120`:
```bash
docker pull ghcr.io/tatertotterson/microwakeword:blackwell
docker pull ghcr.io/tatertotterson/microwakeword:v16-blackwell
docker pull ghcr.io/tatertotterson/microwakeword:v17-blackwell
```
Use the Blackwell image only for RTX 50-series cards. It includes the
@@ -53,9 +53,9 @@ docker run -d \
ghcr.io/tatertotterson/microwakeword:latest
```
Use a version tag such as `ghcr.io/tatertotterson/microwakeword:v16` when you want to pin a known release instead of tracking `latest`.
Use a version tag such as `ghcr.io/tatertotterson/microwakeword:v17` when you want to pin a known release instead of tracking `latest`.
For RTX 50-series cards, use `ghcr.io/tatertotterson/microwakeword:blackwell`
or a pinned tag such as `ghcr.io/tatertotterson/microwakeword:v16-blackwell`
or a pinned tag such as `ghcr.io/tatertotterson/microwakeword:v17-blackwell`
in the same `docker run` command.
The flags:

2
VERSION
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@@ -1 +1 @@
16
17

4
WHATS_NEW.md
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@@ -1,2 +1,2 @@
- Fixed a startup crash caused by newer pip-installed NVIDIA cuBLAS and cuDNN namespace packages not providing a module file path.
- CUDA library discovery now works across both the standard NVIDIA and Blackwell images and safely allows startup when the optional libraries are unavailable.
- Fixed the training-status endpoint crashing after a training log was created because its log-tail limits were missing.
- Restored bounded, incremental training-log updates so the UI can continue showing live progress without repeatedly reading the entire log.

33
tests/test_auto_train.py
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@@ -425,6 +425,39 @@ class AutoTrainTests(unittest.TestCase):
self.assertEqual(trainer.AUTO_TRAIN_STATE["last_stt_device"], "cuda")
self.assertEqual(trainer.AUTO_TRAIN_STATE["last_stt_compute_type"], "float16")
def test_train_status_reads_and_increments_training_log_tail(self):
log_path = Path(self.tempdir.name) / "training.log"
log_path.write_text("first\nsecond\nthird\n", encoding="utf-8")
with trainer.STATE_LOCK:
original_training = dict(trainer.STATE["training"])
trainer.STATE["training"].update(
{
"log_path": str(log_path),
"last_sent_tail": [],
"last_log_size": 0,
}
)
try:
with (
patch.object(trainer, "TRAIN_LOG_TAIL_LINES", 2),
patch.object(trainer, "TRAIN_LOG_MAX_BYTES", 1024),
):
first_status = trainer.train_status()
self.assertEqual(first_status["training"]["log_lines"], ["second", "third"])
self.assertEqual(first_status["training"]["log_text"], "second\nthird")
with log_path.open("a", encoding="utf-8") as log_file:
log_file.write("fourth\n")
next_status = trainer.train_status()
self.assertEqual(next_status["training"]["log_lines"], ["third", "fourth"])
self.assertEqual(next_status["training"]["log_text"], "fourth")
finally:
with trainer.STATE_LOCK:
trainer.STATE["training"].clear()
trainer.STATE["training"].update(original_training)
if __name__ == "__main__":
unittest.main()

2
trainer_server.py
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@@ -70,6 +70,8 @@ PIPER_CATALOG_CACHE_FILE = Path(
str(DATA_DIR / ".cache" / "piper_voices_catalog.json"),
)
).resolve()
TRAIN_LOG_TAIL_LINES = int(os.environ.get("REC_TRAIN_LOG_TAIL_LINES", "400"))
TRAIN_LOG_MAX_BYTES = int(os.environ.get("REC_TRAIN_LOG_MAX_BYTES", str(512 * 1024)))
DATASET_CLEANUP_ARCHIVES = os.environ.get("REC_DATASET_CLEANUP_ARCHIVES", "false").lower() in ("1", "true", "yes", "y")
DATASET_CLEANUP_INTERMEDIATE = os.environ.get("REC_DATASET_CLEANUP_INTERMEDIATE_FILES", "false").lower() in ("1", "true", "yes", "y")