| # Samples |
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|
| Six clips from `samples/`, synthesized by this model on the **Arm CPU alone** |
| (ONNX Runtime, 9 threads) on a DGX Spark GB10. No GPU involved, no cherry |
| picking — these are the first six sentences of the held-out evaluation set in |
| order, the same set the WER/CER numbers in [`EVAL.md`](EVAL.md) come from. |
|
|
| | file | text | |
| |---|---| |
| | `samples/sample_00.wav` | The weather changed suddenly this afternoon. | |
| | `samples/sample_01.wav` | Could you please pass me that book? | |
| | `samples/sample_02.wav` | Wow, I did not expect to see you here! | |
| | `samples/sample_03.wav` | Our meeting has been moved to three o'clock. | |
| | `samples/sample_04.wav` | She walked quietly across the empty room. | |
| | `samples/sample_05.wav` | How many people are coming to dinner tonight? | |
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| 24 kHz mono, 16-bit PCM. |
|
|
| **Listen before trusting any metric.** The word and character error rates in |
| `EVAL.md` measure whether an ASR model can recover the words. They say nothing |
| about naturalness, pacing or timbre. This model is a 3.76M-parameter vocoder |
| distilled from 3 hours of audio, and it sounds like it — the point is that it |
| is 4.8× smaller and 10× faster on an Arm CPU than the teacher, not that it |
| matches it. |
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|
