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--- |
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language: |
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- km |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- openslr |
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- robust-speech-event |
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- km |
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- generated_from_trainer |
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- hf-asr-leaderboard |
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datasets: |
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- openslr |
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model-index: |
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- name: wav2vec2-xls-r-1b-km |
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results: |
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- task: |
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name: Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: OpenSLR km |
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type: openslr |
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args: km |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 32.13 |
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- name: Test CER |
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type: cer |
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value: 9.35 |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: Robust Speech Event - Dev Data |
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type: speech-recognition-community-v2/dev_data |
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args: km |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 32.13 |
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- name: Test CER |
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type: cer |
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value: 9.35 |
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--- |
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# |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the openslr dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4239 |
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- Wer: 0.4221 |
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# Evaluation results on OpenSLR "test" (self-split 10%) (Running ./eval.py): |
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- WER: 0.4490281634272114 |
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- CER: 0.12198285179047481 |
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# Evaluation results on OpenSLR "test" with LM ngram (self-split 10%) (Running ./eval.py): |
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- WER: 0.32130107100357 |
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- CER: 0.09345053678218891 |
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# Note |
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- Since this dataset is small (4 hours of voice recording), we decided not to train that for too long to avoid overfitting and under-generalization. |
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- This model performs worse than its 300M-variant. Probably, we don't explore the hyper-parameter enough? |
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## Installation |
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Install the following libraries on top of HuggingFace Transformers for the supports of language model. |
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``` |
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pip install pyctcdecode |
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pip install https://github.com/kpu/kenlm/archive/master.zip |
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``` |
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## Usage |
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**Approach 1:** Using HuggingFace's pipeline, this will cover everything end-to-end from raw audio input to text output. |
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```python |
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from transformers import pipeline |
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# Load the model |
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pipe = pipeline(model="vitouphy/wav2vec2-xls-r-300m-khmer") |
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# Process raw audio |
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output = pipe("sound_file.wav", chunk_length_s=10, stride_length_s=(4, 2)) |
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``` |
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**Approach 2:** More custom way to predict phonemes. |
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```python |
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from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC |
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import librosa |
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import torch |
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# load model and processor |
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processor = Wav2Vec2Processor.from_pretrained("vitouphy/wav2vec2-xls-r-300m-khmer") |
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model = Wav2Vec2ForCTC.from_pretrained("vitouphy/wav2vec2-xls-r-300m-khmer") |
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# Read and process the input |
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speech_array, sampling_rate = librosa.load("sound_file.wav", sr=16_000) |
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inputs = processor(speech_array, sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits |
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predicted_ids = torch.argmax(logits, axis=-1) |
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predicted_sentences = processor.batch_decode(predicted_ids) |
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print(predicted_sentences) |
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``` |
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## Intended uses & limitations |
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The data used for this model is only around 4 hours of recordings. |
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- We split into 80/10/10. Hence, the training hour is 3.2 hours, which is very very small. |
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- Yet, its performance is not too bad. Quite interesting for such small dataset, actually. You can try it out. |
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- Its limitation is: |
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- Rare characters, e.g. ឬស្សី ឪឡឹក |
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- Speech needs to be clear and articulate. |
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- More data to cover more vocabulary and character may help improve this system. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2000 |
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- num_epochs: 75 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 3.5671 | 5.47 | 400 | 12.0218 | 1.0 | |
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| 3.5159 | 10.95 | 800 | 10.6337 | 1.0 | |
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| 2.4543 | 16.43 | 1200 | 1.8256 | 0.9839 | |
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| 1.9437 | 21.91 | 1600 | 1.1237 | 0.9173 | |
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| 1.696 | 27.39 | 2000 | 0.8246 | 0.7700 | |
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| 1.5342 | 32.87 | 2400 | 0.6433 | 0.6594 | |
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| 1.4509 | 38.35 | 2800 | 0.5500 | 0.5787 | |
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| 1.3478 | 43.83 | 3200 | 0.5070 | 0.4907 | |
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| 1.3096 | 49.31 | 3600 | 0.4692 | 0.4726 | |
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| 1.2532 | 54.79 | 4000 | 0.4448 | 0.4479 | |
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| 1.2291 | 60.27 | 4400 | 0.4374 | 0.4366 | |
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| 1.196 | 65.75 | 4800 | 0.4314 | 0.4310 | |
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| 1.1862 | 71.23 | 5200 | 0.4239 | 0.4221 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.2+cu102 |
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- Datasets 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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