juliensimon/wav2vec2-conformer-rel-pos-large-finetuned-speech-commands
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'}]You can also use them with the ``Auto``API:
>>> import torch, librosa
>>> from transformers import AutoModelForAudioClassification, Wav2Vec2FeatureExtractor
>>> feature_extractor = Wav2Vec2FeatureExtractor()
>>> model = AutoModelForAudioClassification.from_pretrained("juliensimon/wav2vec2-conformer-rel-pos-large-finetuned-speech-commands")
>>> audio, rate = librosa.load("up16k.wav", sr = 16000)
>>> inputs = feature_extractor(audio, sampling_rate=16000, return_tensors = "pt")
>>> logits = model(inputs['input_values'])
>>> logits
SequenceClassifierOutput(loss=None, logits=tensor([[-0.4635, -1.0112, 4.7935, 0.8528, 1.6265, 0.6456, 1.5423, 2.0132,
1.6103, 0.5847, -2.2526, 0.8839, 0.8163, -1.5655, -1.4160, -0.4196,
-0.1097, -1.8827, 0.6609, -0.2022, 0.0971, -0.6205, 0.4492, 0.0926,
-2.4848, 0.2630, -0.4584, -2.4327, -1.1654, 0.3897, -0.3374, -1.2418,
-0.1045, 0.2827, -1.5667, -0.0963]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)
>>> classes = torch.softmax(logits.logits, dim=1)
>>> torch.set_printoptions(precision=3, sci_mode=False)
>>> classes
tensor([[ 0.004, 0.002, 0.701, 0.014, 0.030, 0.011,
0.027, 0.043, 0.029, 0.010, 0.001, 0.014,
0.013, 0.001, 0.001, 0.004, 0.005, 0.001,
0.011, 0.005, 0.006, 0.003, 0.009, 0.006,
0.000, 0.008, 0.004, 0.001, 0.002, 0.009,
0.004, 0.002, 0.005, 0.008, 0.001, 0.005]],
grad_fn=<SoftmaxBackward0>)
>>> top_class = torch.argmax(logits.logits, dim=1)
>>> top_class
tensor([2])
>>> model.config.id2label[top_class.numpy()[0]]
'up'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
- trainbatchsize: 256
- evalbatchsize: 256
- seed: 42
- gradientaccumulationsteps: 4
- totaltrainbatch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lrschedulertype: linear
- lrschedulerwarmup_ratio: 0.1
- num_epochs: 10
Training results
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1