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--- |
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language: ru |
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datasets: |
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- SberDevices/Golos |
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- common_voice |
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metrics: |
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- wer |
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- cer |
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tags: |
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- audio |
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- automatic-speech-recognition |
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- speech |
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- common_voice |
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- SberDevices/Golos |
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license: apache-2.0 |
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widget: |
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- example_title: test Russian speech "нейросети это хорошо" (in English, "neural networks are good") |
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src: https://huggingface.co/bond005/wav2vec2-large-ru-golos-with-lm/resolve/main/test_sound_ru.flac |
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model-index: |
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- name: XLSR Wav2Vec2 Russian with Language Model by Ivan Bondarenko |
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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: Sberdevices Golos (crowd) |
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type: SberDevices/Golos |
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args: ru |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 4.272 |
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- name: Test CER |
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type: cer |
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value: 0.983 |
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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: Sberdevices Golos (farfield) |
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type: SberDevices/Golos |
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args: ru |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 11.405 |
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- name: Test CER |
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type: cer |
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value: 3.628 |
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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: Common Voice ru |
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type: common_voice |
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args: ru |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 19.053 |
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- name: Test CER |
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type: cer |
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value: 4.876 |
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--- |
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# Wav2Vec2-Large-Ru-Golos-With-LM |
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The Wav2Vec2 model is based on [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53), fine-tuned in Russian using [Sberdevices Golos](https://huggingface.co/datasets/SberDevices/Golos) with audio augmentations like as pitch shift, acceleration/deceleration of sound, reverberation etc. |
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The 2-gram language model is built on the Russian text corpus obtained from three open sources: |
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- random 10% subset of [Taiga](https://tatianashavrina.github.io/taiga_site) |
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- [Russian Wikipedia](https://ru.wikipedia.org) |
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- [Russian Wikinews](https://ru.wikinews.org). |
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## Usage |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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You can use this model by writing your own inference script: |
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```python |
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import os |
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import warnings |
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import librosa |
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import nltk |
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import numpy as np |
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import torch |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2ProcessorWithLM |
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MODEL_ID = "bond005/wav2vec2-large-ru-golos-with-lm" |
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DATASET_ID = "bond005/sberdevices_golos_10h_crowd" |
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SAMPLES = 20 |
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nltk.download('punkt') |
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num_processes = max(1, os.cpu_count()) |
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test_dataset = load_dataset(DATASET_ID, split=f"test[:{SAMPLES}]") |
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processor = Wav2Vec2ProcessorWithLM.from_pretrained(MODEL_ID) |
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) |
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# Preprocessing the datasets. |
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# We need to read the audio files as arrays |
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def speech_file_to_array_fn(batch): |
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speech_array = batch["audio"]["array"] |
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batch["speech"] = np.asarray(speech_array, dtype=np.float32) |
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return batch |
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removed_columns = set(test_dataset.column_names) |
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removed_columns -= {'transcription', 'speech'} |
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removed_columns = sorted(list(removed_columns)) |
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with warnings.catch_warnings(): |
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warnings.simplefilter("ignore") |
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test_dataset = test_dataset.map( |
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speech_file_to_array_fn, |
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num_proc=num_processes, |
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remove_columns=removed_columns |
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) |
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inputs = processor(test_dataset["speech"], sampling_rate=16_000, |
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return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values, |
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attention_mask=inputs.attention_mask).logits |
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predicted_sentences = processor.batch_decode( |
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logits=logits.numpy(), |
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num_processes=num_processes |
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).text |
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with warnings.catch_warnings(): |
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warnings.simplefilter("ignore") |
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for i, predicted_sentence in enumerate(predicted_sentences): |
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print("-" * 100) |
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print("Reference:", test_dataset[i]["transcription"]) |
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print("Prediction:", predicted_sentence) |
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``` |
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```text |
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---------------------------------------------------------------------------------------------------- |
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Reference: шестьдесят тысяч тенге сколько будет стоить |
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Prediction: шестьдесят тысяч тенге сколько будет стоить |
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---------------------------------------------------------------------------------------------------- |
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Reference: покажи мне на смотрешке телеканал синергия тв |
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Prediction: покажи мне на смотрешке телеканал синергия тв |
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---------------------------------------------------------------------------------------------------- |
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Reference: заказать яблоки зеленые |
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Prediction: заказать яблоки зеленые |
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---------------------------------------------------------------------------------------------------- |
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Reference: алиса закажи килограммовый торт графские развалины |
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Prediction: алиса закажи килограммовый торт графские развалины |
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---------------------------------------------------------------------------------------------------- |
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Reference: ищи телеканал про бизнес на тиви |
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Prediction: ищи телеканал про бизнес на тви |
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---------------------------------------------------------------------------------------------------- |
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Reference: михаила мурадяна |
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Prediction: михаила мурадяна |
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---------------------------------------------------------------------------------------------------- |
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Reference: любовницы две тысячи тринадцать пятнадцатый сезон |
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Prediction: любовница две тысячи тринадцать пятнадцатый сезон |
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---------------------------------------------------------------------------------------------------- |
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Reference: найди боевики |
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Prediction: найди боевики |
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---------------------------------------------------------------------------------------------------- |
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Reference: гетто сезон три |
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Prediction: гетта сезон три |
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---------------------------------------------------------------------------------------------------- |
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Reference: хочу посмотреть ростов папа на телевизоре |
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Prediction: хочу посмотреть ростов папа на телевизоре |
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---------------------------------------------------------------------------------------------------- |
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Reference: сбер какое твое самое ненавистное занятие |
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Prediction: сбер какое твое самое ненавистное занятие |
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---------------------------------------------------------------------------------------------------- |
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Reference: афина чем платят у китайцев |
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Prediction: афина чем платят у китайцев |
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---------------------------------------------------------------------------------------------------- |
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Reference: джой как работает досрочное погашение кредита |
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Prediction: джой как работает досрочное погашение кредита |
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---------------------------------------------------------------------------------------------------- |
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Reference: у тебя найдется люк кейдж |
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Prediction: у тебя найдется люк кейдж |
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---------------------------------------------------------------------------------------------------- |
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Reference: у тебя будет лучшая часть пинк |
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Prediction: у тебя будет лучшая часть пинк |
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---------------------------------------------------------------------------------------------------- |
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Reference: пожалуйста пополните мне счет |
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Prediction: пожалуйста пополните мне счет |
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---------------------------------------------------------------------------------------------------- |
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Reference: анне павловне шабуровой |
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Prediction: анне павловне шабуровой |
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---------------------------------------------------------------------------------------------------- |
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Reference: врубай на смотрешке муз тв |
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Prediction: врубай на смотрешке муз тиви |
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---------------------------------------------------------------------------------------------------- |
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Reference: найди на смотрешке лдпр тв |
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Prediction: найди на смотрешке лдпр тв |
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---------------------------------------------------------------------------------------------------- |
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Reference: сбер мне нужен педикюр забей мне место |
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Prediction: сбер мне нужен педикюр забелье место |
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``` |
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The Google Colab version of [this script](https://colab.research.google.com/drive/1SnQmrt6HmMNV-zK-UCPajuwl1JvoCqbX?usp=sharing) is available too. |
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## Evaluation |
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This model was evaluated on the test subsets of [SberDevices Golos](https://huggingface.co/datasets/SberDevices/Golos) and [Common Voice 6.0](https://huggingface.co/datasets/common_voice) (Russian part), but it was trained on the training subset of SberDevices Golos only. |
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## Citation |
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If you want to cite this model you can use this: |
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```bibtex |
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@misc{bondarenko2022wav2vec2-large-ru-golos, |
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title={XLSR Wav2Vec2 Russian with 2-gram Language Model by Ivan Bondarenko}, |
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author={Bondarenko, Ivan}, |
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publisher={Hugging Face}, |
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journal={Hugging Face Hub}, |
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howpublished={\url{https://huggingface.co/bond005/wav2vec2-large-ru-golos-with-lm}}, |
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year={2022} |
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} |
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``` |
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