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. |