Dataset Viewer
id
stringlengths 4
117
| sentence
stringlengths 1
134k
|
|---|---|
jonatasgrosman/wav2vec2-large-xlsr-53-english | Fine-tuned XLSR-53 large model for speech recognition in English |
jonatasgrosman/wav2vec2-large-xlsr-53-english | Fine-tuned facebook/wav2vec2-large-xlsr-53 on English using the train and validation splits of Common Voice 6.1. |
jonatasgrosman/wav2vec2-large-xlsr-53-english | When using this model, make sure that your speech input is sampled at 16kHz. |
jonatasgrosman/wav2vec2-large-xlsr-53-english | This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) |
jonatasgrosman/wav2vec2-large-xlsr-53-english | The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint |
jonatasgrosman/wav2vec2-large-xlsr-53-english | Usage |
jonatasgrosman/wav2vec2-large-xlsr-53-english | The model can be used directly (without a language model) as follows... |
jonatasgrosman/wav2vec2-large-xlsr-53-english | Using the HuggingSound library: |
jonatasgrosman/wav2vec2-large-xlsr-53-english | from huggingsound import SpeechRecognitionModel model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-english") audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] transcriptions = model.transcribe(audio_paths) |
jonatasgrosman/wav2vec2-large-xlsr-53-english | Writing your own inference script: |
jonatasgrosman/wav2vec2-large-xlsr-53-english | import torch import librosa from datasets import load_dataset from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor LANG_ID |
jonatasgrosman/wav2vec2-large-xlsr-53-english | = "en" MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-english" SAMPLES = 10 test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) |
jonatasgrosman/wav2vec2-large-xlsr-53-english | # Preprocessing the datasets. |
jonatasgrosman/wav2vec2-large-xlsr-53-english | # We need to read the audio files as arrays def speech_file_to_array_fn(batch): speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) batch["speech"] = speech_array batch["sentence"] = batch["sentence"].upper() return batch test_dataset |
jonatasgrosman/wav2vec2-large-xlsr-53-english | = test_dataset.map(speech_file_to_array_fn) inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) with torch.no_grad(): logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits predicted_ids = torch.argmax(logits, dim=-1) predicted_sentences = processor.batch_decode(predicted_ids) for i, predicted_sentence in enumerate(predicted_sentences): print("-" * 100) print("Reference:", test_dataset[i]["sentence"]) print("Prediction:", predicted_sentence) |
jonatasgrosman/wav2vec2-large-xlsr-53-english | Reference |
jonatasgrosman/wav2vec2-large-xlsr-53-english | Prediction |
jonatasgrosman/wav2vec2-large-xlsr-53-english | "SHE'LL BE ALL RIGHT." |
jonatasgrosman/wav2vec2-large-xlsr-53-english | SHE'LL BE ALL RIGHT |
jonatasgrosman/wav2vec2-large-xlsr-53-english | SIX |
jonatasgrosman/wav2vec2-large-xlsr-53-english | SIX |
jonatasgrosman/wav2vec2-large-xlsr-53-english | "ALL'S WELL |
jonatasgrosman/wav2vec2-large-xlsr-53-english | THAT ENDS WELL." |
jonatasgrosman/wav2vec2-large-xlsr-53-english | ALL AS WELL THAT ENDS |
jonatasgrosman/wav2vec2-large-xlsr-53-english | WELL |
jonatasgrosman/wav2vec2-large-xlsr-53-english | DO YOU MEAN IT? |
jonatasgrosman/wav2vec2-large-xlsr-53-english | DO YOU MEAN IT |
jonatasgrosman/wav2vec2-large-xlsr-53-english | THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE, BUT STILL CAUSES REGRESSIONS. |
jonatasgrosman/wav2vec2-large-xlsr-53-english | THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE |
jonatasgrosman/wav2vec2-large-xlsr-53-english | BUT STILL CAUSES REGRESSION |
jonatasgrosman/wav2vec2-large-xlsr-53-english | HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? |
jonatasgrosman/wav2vec2-large-xlsr-53-english | HOW IS MOSLILLAR GOING TO HANDLE ANDBEWOOTH HIS LIKE Q AND Q |
jonatasgrosman/wav2vec2-large-xlsr-53-english | " |
jonatasgrosman/wav2vec2-large-xlsr-53-english | I GUESS YOU MUST THINK I'M KINDA BATTY." |
jonatasgrosman/wav2vec2-large-xlsr-53-english | RUSTIAN WASTIN PAN ONTE |
jonatasgrosman/wav2vec2-large-xlsr-53-english | BATTLY |
jonatasgrosman/wav2vec2-large-xlsr-53-english | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING? |
jonatasgrosman/wav2vec2-large-xlsr-53-english | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING |
jonatasgrosman/wav2vec2-large-xlsr-53-english | SAUCE |
jonatasgrosman/wav2vec2-large-xlsr-53-english | FOR THE GOOSE IS SAUCE FOR THE GANDER. |
jonatasgrosman/wav2vec2-large-xlsr-53-english | SAUCE FOR THE GUICE IS SAUCE FOR THE GONDER |
jonatasgrosman/wav2vec2-large-xlsr-53-english | GROVES STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD. |
jonatasgrosman/wav2vec2-large-xlsr-53-english | GRAFS STARTED WRITING SONGS WHEN SHE WAS |
jonatasgrosman/wav2vec2-large-xlsr-53-english | FOUR YEARS OLD |
jonatasgrosman/wav2vec2-large-xlsr-53-english | Evaluation |
jonatasgrosman/wav2vec2-large-xlsr-53-english | To evaluate on mozilla-foundation/common_voice_6_0 with split test |
jonatasgrosman/wav2vec2-large-xlsr-53-english | python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-english --dataset mozilla-foundation/common_voice_6_0 --config en --split test |
jonatasgrosman/wav2vec2-large-xlsr-53-english | To evaluate on speech-recognition-community-v2/dev_data |
jonatasgrosman/wav2vec2-large-xlsr-53-english | python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-english --dataset speech-recognition-community-v2/dev_data --config |
jonatasgrosman/wav2vec2-large-xlsr-53-english | en --split validation --chunk_length_s 5.0 --stride_length_s 1.0 |
jonatasgrosman/wav2vec2-large-xlsr-53-english | Citation |
jonatasgrosman/wav2vec2-large-xlsr-53-english | If you want to cite this model you can use this: |
jonatasgrosman/wav2vec2-large-xlsr-53-english | @misc{grosman2021xlsr53-large-english, title={Fine-tuned {XLSR}-53 large model for speech recognition in {E}nglish}, author={Grosman, Jonatas}, howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-english}}, year={2021} } |
bert-base-uncased | BERT base model (uncased) |
bert-base-uncased | Pretrained model on English language using a masked language modeling (MLM) objective. |
bert-base-uncased | It was introduced in this paper and first released in this repository. |
bert-base-uncased | This model is uncased: it does not make a difference between english and English. |
bert-base-uncased | Disclaimer: |
bert-base-uncased | The team releasing BERT did not write a model card for this model so this model card has been written by the Hugging Face team. |
bert-base-uncased | Model description |
bert-base-uncased | BERT is a transformers model pretrained on a large corpus of English data in a self-supervised fashion. |
bert-base-uncased | This means it was pretrained on the raw texts only, with no humans labeling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. |
bert-base-uncased | More precisely, it was pretrained with two objectives: |
bert-base-uncased | Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. |
bert-base-uncased | This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally masks the future tokens. |
bert-base-uncased | It allows the model to learn a bidirectional representation of the sentence. |
bert-base-uncased | Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. |
bert-base-uncased | Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. |
bert-base-uncased | The model then has to predict if the two sentences were following each other or not. |
bert-base-uncased | This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences, for instance, you can train a standard classifier using the features produced by the BERT model as inputs. |
bert-base-uncased | Model variations |
bert-base-uncased | BERT has originally been released in base and large variations, for cased and uncased input text. |
bert-base-uncased | The uncased models also strips out an accent markers. |
bert-base-uncased | Chinese and multilingual uncased and cased versions followed shortly after. |
bert-base-uncased | Modified preprocessing with whole word masking has replaced subpiece masking in a following work, with the release of two models. |
bert-base-uncased | Other 24 smaller models are released afterward. |
bert-base-uncased | The detailed release history can be found on the google-research/bert readme on github. |
bert-base-uncased | Model |
bert-base-uncased | #params |
bert-base-uncased | Language |
bert-base-uncased | bert-base-uncased |
bert-base-uncased | 110M |
bert-base-uncased | English |
bert-base-uncased | bert-large-uncased |
bert-base-uncased | 340 |
bert-base-uncased | M |
bert-base-uncased | English |
bert-base-uncased | bert-base-cased |
bert-base-uncased | 110M |
bert-base-uncased | English |
bert-base-uncased | bert-large-cased |
bert-base-uncased | 340M |
bert-base-uncased | English |
bert-base-uncased | bert-base-chinese |
bert-base-uncased | 110M |
bert-base-uncased | Chinese |
bert-base-uncased | bert-base-multilingual-cased |
bert-base-uncased | 110M |
bert-base-uncased | Multiple |
bert-base-uncased | bert-large-uncased-whole-word-masking |
End of preview. Expand
in Data Studio
README.md exists but content is empty.
- Downloads last month
- 37