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This is a finetuned version of facebook/wav2vec2-large-xlsr-53 trained on the Amharic Speech Corpus. This corpus was produced by Abate et al. (2005) (10.21437/Interspeech.2005-467). The model achieves a WER of 26% and a CER of 7% on the validation set of the Amharic Readspeech data.

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GigaAM Multilingual is a family of Conformer-based foundation models (220M / 600M parameters) pre-trained with a HuBERT-style objective on **2M hours** of speech across **70+ languages** and fine-tuned for speech recognition with character-wise CTC decoders on 50K hours. The models provide best-in-class open-source quality on Russian, Kazakh, Kyrgyz, and Uzbek, and moderate quality on English. GigaAM Multilingual includes the following model variants: - `ssl` — 220M self-supervised encoder - `ctc` — 220M ASR model with a character-wise CTC decoder - `large_ssl` — 600M self-supervised encoder - `large_ctc` — 600M ASR model with a character-wise CTC decoder

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GigaAM-v3 is a Conformer-based foundation model with 220–240M parameters, pretrained on diverse Russian speech data using the HuBERT-CTC objective. It is the third generation of the GigaAM family and provides state-of-the-art performance on Russian ASR across a wide range of domains. GigaAM-v3 includes the following model variants: - `ssl` — self-supervised HuBERT–CTC encoder pre-trained on 700,000 hours of Russian speech - `ctc` — ASR model fine-tuned with a CTC decoder - `rnnt` — ASR model fine-tuned with an RNN-T decoder - `e2e_ctc` — end-to-end CTC model with punctuation and text normalization - `e2e_rnnt` — end-to-end RNN-T model with punctuation and text normalization `GigaAM-v3` training incorporates new internal datasets: callcenter conversations, speech with background music, natural speech, and speech with atypical characteristics. the models perform on average **30%** better on these new domains, while maintaining the same quality as previous GigaAM generations on public benchmarks.

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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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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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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.

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