wav2vec2-large-xlsr-georgian

Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Georgian using the [Common Voice](https://huggingface.co/datasets/common_voice). 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: import librosa import torch import torchaudio from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor test_dataset = load_dataset("common_voice", "ka", split="test[:2%]") processor = Wav2Vec2Processor.from_pretrained("xsway/wav2vec2-large-xlsr-georgian") model = Wav2Vec2ForCTC.from_pretrained("xsway/wav2vec2-large-xlsr-georgian") resampler = lambda sampling_rate, y: librosa.resample(y.numpy().squeeze(), sampling_rate, 16_000) def speech_file_to_array_fn(batch): \\\\tspeech_array, sampling_rate = torchaudio.load(batch["path"]) \\\\tbatch["speech"] = resampler(sampling_rate, speech_array).squeeze() \\\\treturn batch test_dataset = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): \\\\tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) print("Prediction:", processor.batch_decode(predicted_ids)) print("Reference:", test_dataset["sentence"][:2]) The model can be evaluated as follows on the Georgian test data of Common Voice. import torch import torchaudio from datasets import load_dataset, load_metric from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor import re import librosa test_dataset = load_dataset("common_voice", "ka", split="test") wer = load_metric("wer") processor = Wav2Vec2Processor.from_pretrained("xsway/wav2vec2-large-xlsr-georgian") model = Wav2Vec2ForCTC.from_pretrained("xsway/wav2vec2-large-xlsr-georgian") model.to("cuda") chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“]' resampler = lambda sampling_rate, y: librosa.resample(y.numpy().squeeze(), sampling_rate, 16_000) def speech_file_to_array_fn(batch): batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower() speech_array, sampling_rate = torchaudio.load(batch["path"]) batch["speech"] = resampler(sampling_rate, speech_array).squeeze() return batch test_dataset = test_dataset.map(speech_file_to_array_fn) def evaluate(batch): inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits pred_ids = torch.argmax(logits, dim=-1) batch["pred_strings"] = processor.batch_decode(pred_ids) return batch result = test_dataset.map(evaluate, batched=True, batch_size=8) print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"]))) **Test Result**: 45.28 % The Common Voice `train`, `validation` datasets were used for training. The script used for training can be found [here](...)