| |
| """ |
| Plot the confusion matrix for whispers, voiced, and reverberant |
| whispers side by side. Writes to whisper_figures/confusion_3way.png. |
| |
| Run: python examples/confusion_plot.py |
| """ |
| import os |
| import numpy as np |
| import matplotlib |
| matplotlib.use("Agg") |
| import matplotlib.pyplot as plt |
| from whisper_decoder import ( |
| MLP, train_mlp, generate_corpus, FEATURE_DIM, WORD_ORDER, |
| ) |
|
|
| OUT = "whisper_figures" |
|
|
|
|
| def confusion(model, corpus, mu, sigma): |
| X = (corpus.X_test - mu) / sigma |
| pred = model.predict(X) |
| cm = np.zeros((10, 10), dtype=int) |
| for t, p in zip(corpus.y_test, pred): |
| cm[t, p] += 1 |
| return cm, float((pred == corpus.y_test).mean()) |
|
|
|
|
| def main(): |
| os.makedirs(OUT, exist_ok=True) |
|
|
| print("training...") |
| train = generate_corpus('whisper', 150, 30, seed=0, |
| reverb_train_frac=0.25) |
| mu = train.X_train.mean(axis=0) |
| sigma = train.X_train.std(axis=0) + 1e-9 |
| X_tr = (train.X_train - mu) / sigma |
|
|
| model = MLP(FEATURE_DIM, 64, 32, 10, seed=0) |
| train_mlp(model, X_tr, train.y_train, epochs=200, |
| batch=64, lr=3e-3, seed=0) |
|
|
| print("evaluating on three conditions...") |
| whisper = generate_corpus('whisper', 10, 30, seed=10) |
| voiced = generate_corpus('voiced', 10, 30, seed=11) |
| reverb = generate_corpus('whisper', 10, 30, seed=12, reverb=True) |
|
|
| fig, axes = plt.subplots(1, 3, figsize=(18, 5.5)) |
| conditions = [ |
| ("whisper (in-distribution)", whisper), |
| ("voiced (domain shift)", voiced), |
| ("reverberant whisper", reverb), |
| ] |
| for ax, (title, corpus) in zip(axes, conditions): |
| cm, acc = confusion(model, corpus, mu, sigma) |
| im = ax.imshow(cm, cmap="Blues", aspect="auto", |
| vmin=0, vmax=cm.max()) |
| ax.set_xticks(np.arange(10)) |
| ax.set_yticks(np.arange(10)) |
| ax.set_xticklabels(WORD_ORDER, rotation=45, fontsize=8) |
| ax.set_yticklabels(WORD_ORDER, fontsize=8) |
| for i in range(10): |
| for j in range(10): |
| if cm[i, j] > 0: |
| ax.text(j, i, str(cm[i, j]), |
| ha="center", va="center", |
| fontsize=7, |
| color="white" if cm[i, j] > cm.max() / 2 |
| else "black") |
| ax.set_xlabel("predicted") |
| ax.set_ylabel("true") |
| ax.set_title(f"{title}\naccuracy {acc*100:.1f}%", |
| fontsize=11) |
| plt.colorbar(im, ax=ax, shrink=0.7) |
|
|
| plt.tight_layout() |
| path = os.path.join(OUT, "confusion_3way.png") |
| plt.savefig(path, dpi=130, bbox_inches="tight") |
| plt.close() |
| print(f"saved: {path}") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
