Tiny Wav2letter is a tiny version of the original Wav2Letter model. It is a convolutional speech recognition neural network. This implementation was created by Arm, pruned to 50% sparsity, fine-tuned and quantized using the TensorFlow Model Optimization Toolkit.
Apache-2.0
| Platform |
Optimized |
| Cortex-A |
✔️ |
| Cortex-M |
✔️ |
| Mali GPU |
✖️ |
| Ethos U |
✔️ |
- ✔️ - Will run on this platform.
- ✖️ - Will not run on this platform.
Dataset: Fluent Speech (trianed on LibriSpeech,Mini LibrySpeech,Fluent Speech)
Please note that Fluent Speech dataset hosted on Kaggle is a licensed dataset.
| Metric |
Value |
| LER |
0.0348 |
| WER |
0.112 |
| Optimization |
Value |
| Quantization |
INT8 |
| Input Node Name |
Shape |
Description |
| input_1_int8 |
(1, 296, 39) |
Speech converted to MFCCs and quantized to INT8 |
| Output Node Name |
Shape |
Description |
| Identity_int8 |
(1, 1, 148, 29) |
A tensor of time and class probabilities, that represents the probability of each class at each timestep. Should be passed to a decoder. For example ctc_beam_search_decoder. |