license: apache-2.0
language: en
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: wav2vec2-conformer-rel-pos-large-finetuned-speech-commands
results:
- task:
type: audio-classification
name: audio classification
dataset:
type: speech_commands
name: speech_commands
split: v0.02
metrics:
- type: accuracy
value: 0.9724
name: accuracy
wav2vec2-conformer-rel-pos-large-finetuned-speech-commands
Model description
This model is a fine-tuned version of facebook/wav2vec2-conformer-rel-pos-large on the speech_commands dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5245
- Accuracy: 0.9724
Intended uses & limitations
The model can spot one of the following keywords: "Yes", "No", "Up", "Down", "Left", "Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine", "Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow", "Backward", "Forward", "Follow", "Learn", "Visual".
The repository includes sample files that I recorded (WAV, 16Khz sampling rate, mono). The simplest way to use the model is with the pipeline API:
>>> from transformers import pipeline
>>> p = pipeline("audio-classification", model="juliensimon/wav2vec2-conformer-rel-pos-large-finetuned-speech-commands")
>>> p("up16k.wav")
[{'score': 0.7008192539215088, 'label': 'up'}, {'score': 0.04346614331007004, 'label': 'off'}, {'score': 0.029526518657803535, 'label': 'left'}, {'score': 0.02905120886862278, 'label': 'stop'}, {'score': 0.027142534032464027, 'label': 'on'}]
>>> p("stop16k.wav")
[{'score': 0.6969656944274902, 'label': 'stop'}, {'score': 0.03391443192958832, 'label': 'up'}, {'score': 0.027382319793105125, 'label': 'seven'}, {'score': 0.020835857838392258, 'label': 'five'}, {'score': 0.018051736056804657, 'label': 'down'}]
>>> p("marvin16k.wav")
[{'score': 0.5276530981063843, 'label': 'marvin'}, {'score': 0.04645705968141556, 'label': 'down'}, {'score': 0.038583893328905106, 'label': 'backward'}, {'score': 0.03578080236911774, 'label': 'wow'}, {'score': 0.03178196772933006, 'label': 'bird'}]
Training and evaluation data
- subset: v0.02
- full training set
- full validation set
Training procedure
The model was fine-tuned on Amazon SageMaker, using an ml.p3dn.24xlarge instance (8 NVIDIA V100 GPUs). Total training time for 10 epochs was 4.5 hours.
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 2.2901 | 1.0 | 83 | 2.0542 | 0.8875 |
| 1.8375 | 2.0 | 166 | 1.5610 | 0.9316 |
| 1.4957 | 3.0 | 249 | 1.1850 | 0.9558 |
| 1.1917 | 4.0 | 332 | 0.9159 | 0.9695 |
| 1.0449 | 5.0 | 415 | 0.7624 | 0.9687 |
| 0.9319 | 6.0 | 498 | 0.6444 | 0.9715 |
| 0.8559 | 7.0 | 581 | 0.5806 | 0.9711 |
| 0.8199 | 8.0 | 664 | 0.5394 | 0.9721 |
| 0.7949 | 9.0 | 747 | 0.5245 | 0.9724 |
| 0.7975 | 10.0 | 830 | 0.5256 | 0.9721 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1