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Fine-tuned facebook/wav2vec2-large-xlsr-53 for Kazakh ASR using the Kazakh Speech Corpus v1.1 The model can be evaluated as follows on the test set of Kazakh Speech Corpus v1.1. To evaluate, download the archive, untar and pass the path to data to `get_test_dataset` as below:
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This model is a fine-tuned version of alakxender/w2v-bert-2.0-dhivehi-cv on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.3580 - Wer: 0.4591 The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP
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Fine-tuned version of facebook/wav2vec2-base on `mozilla-foundation/common_voice_8_0 be` dataset. `Train`, `Dev`, `Test` splits were used as they are present in the dataset. No additional data was used from `Validated` split, only 1 voicing of each sentence was used - the way the data was split by CommonVoice CorporaCreator. To build a better model **one can use additional voicings from `Validated` split** for sentences already present in `Train`, `Dev`, `Test` splits, i.e. enlarge mentioned splits. Language model was built using KenLM. 5-gram Language model was built on sentences from `Train + (Other - Dev - Test)` splits of `mozilla-foundation/common_voice_8_0 be` dataset.
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This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_albanian dataset. The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP - Transformers 4.35.2 - Pytorch 2.1.0+cu118 - Datasets 2.14.7 - Tokenizers 0.15.0
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This model is a fine-tuned version of facebook/w2v-bert-2.0. This has a CER of 10.27 on Common Voice 16 (yue) test set (without punctuations). For training, three datasets were used: - Common Voice 16 `zh-HK` and `yue` Train Set - CantoMap: Winterstein, Grégoire, Tang, Carmen and Lai, Regine (2020) "CantoMap: a Hong Kong Cantonese MapTask Corpus", in Proceedings of The 12th Language Resources and Evaluation Conference, Marseille: European Language Resources Association, p. 2899-2906. - Cantonse-ASR: Yu, Tiezheng, Frieske, Rita, Xu, Peng, Cahyawijaya, Samuel, Yiu, Cheuk Tung, Lovenia, Holy, Dai, Wenliang, Barezi, Elham, Chen, Qifeng, Ma, Xiaojuan, Shi, Bertram, Fung, Pascale (2022) "Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset", 2022. Link: https://arxiv.org/pdf/2201.02419.pdf
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Fine-tuned facebook/wav2vec2-large-xlsr-53 on Kannada using the OpenSLR SLR79 dataset. When using this model, make sure that your speech input is sampled at 16kHz. The model can be used directly (without a language model) as follows, assuming you have a dataset with Kannada `sentence` and `path` fields: The colab notebook used for training can be found here.
Fine-tuned facebook/wav2vec2-large-xlsr-53 in Tamil using the Common Voice When using this model, make sure that your speech input is sampled at 16kHz.
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Fine-tuned facebook/wav2vec2-large-xlsr-53 on Estonian using the Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz.
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