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--- |
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language: en |
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datasets: |
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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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- xlsr-fine-tuning-week |
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license: apache-2.0 |
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model-index: |
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- name: XLSR Wav2Vec2 English by Jonatas Grosman |
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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: Common Voice en |
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type: common_voice |
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args: en |
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metrics: |
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- name: Test WER |
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type: wer |
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value: 18.98 |
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- name: Test CER |
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type: cer |
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value: 8.29 |
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--- |
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# Wav2Vec2-Large-XLSR-53-English |
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on English using the [Common Voice](https://huggingface.co/datasets/common_voice). |
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When using this model, make sure that your speech input is sampled at 16kHz. |
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This model has been fine-tuned thanks to the GPU credits generously given by the [OVHcloud](https://www.ovhcloud.com/en/public-cloud/ai-training/) :) |
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The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint |
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## Usage |
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The model can be used directly (without a language model) as follows: |
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```python |
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import torch |
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import librosa |
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from datasets import load_dataset |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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LANG_ID = "en" |
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-english" |
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SAMPLES = 10 |
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test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]") |
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processor = Wav2Vec2Processor.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, sampling_rate = librosa.load(batch["path"], sr=16_000) |
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batch["speech"] = speech_array |
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batch["sentence"] = batch["sentence"].upper() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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inputs = processor(test_dataset["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits |
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predicted_ids = torch.argmax(logits, dim=-1) |
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predicted_sentences = processor.batch_decode(predicted_ids) |
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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]["sentence"]) |
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print("Prediction:", predicted_sentence) |
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``` |
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| Reference | Prediction | |
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| ------------- | ------------- | |
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| "SHE'LL BE ALL RIGHT." | SHE'LL BE ALL RIGHT | |
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| SIX | SIX | |
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| "ALL'S WELL THAT ENDS WELL." | ALL AS WELL THAT ENDS WELL | |
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| DO YOU MEAN IT? | DO YOU MEAN IT | |
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| THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE, BUT STILL CAUSES REGRESSIONS. | THE NEW PATCH IS LESS INVASIVE THAN THE OLD ONE BUT STILL CAUSES REGRESSION | |
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| HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? | HOW IS MOSLILLAR GOING TO HANDLE ANDBEWOOTH HIS LIKE Q AND Q | |
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| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RUSTIAN WASTIN PAN ONTE BATTLY | |
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| NO ONE NEAR THE REMOTE MACHINE YOU COULD RING? | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING | |
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| SAUCE FOR THE GOOSE IS SAUCE FOR THE GANDER. | SAUCE FOR THE GUICE IS SAUCE FOR THE GONDER | |
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| GROVES STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD. | GRAFS STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD | |
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## Evaluation |
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The model can be evaluated as follows on the English test data of Common Voice. |
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```python |
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import torch |
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import re |
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import librosa |
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from datasets import load_dataset, load_metric |
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from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor |
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LANG_ID = "en" |
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-english" |
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DEVICE = "cuda" |
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CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "�", "ʿ", "·", "჻", "~", "՞", |
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"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘", "’", "《", "》", "(", ")", "[", "]", |
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"{", "}", "=", "`", "_", "+", "<", ">", "…", "–", "°", "´", "ʾ", "‹", "›", "©", "®", "—", "→", "。", |
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"、", "﹂", "﹁", "‧", "～", "﹏", "，", "｛", "｝", "（", "）", "［", "］", "【", "】", "‥", "〽", |
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"『", "』", "〝", "〟", "⟨", "⟩", "〜", "：", "！", "？", "♪", "؛", "/", "\\", "º", "−", "^", "ʻ", "ˆ"] |
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test_dataset = load_dataset("common_voice", LANG_ID, split="test") |
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wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py |
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cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py |
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chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]" |
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processor = Wav2Vec2Processor.from_pretrained(MODEL_ID) |
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model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID) |
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model.to(DEVICE) |
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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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with warnings.catch_warnings(): |
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warnings.simplefilter("ignore") |
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speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000) |
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batch["speech"] = speech_array |
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batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["sentence"]).upper() |
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return batch |
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test_dataset = test_dataset.map(speech_file_to_array_fn) |
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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 evaluate(batch): |
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inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True) |
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with torch.no_grad(): |
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logits = model(inputs.input_values.to(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).logits |
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pred_ids = torch.argmax(logits, dim=-1) |
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batch["pred_strings"] = processor.batch_decode(pred_ids) |
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return batch |
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result = test_dataset.map(evaluate, batched=True, batch_size=8) |
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predictions = [x.upper() for x in result["pred_strings"]] |
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references = [x.upper() for x in result["sentence"]] |
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print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") |
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print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}") |
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``` |
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**Test Result**: |
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In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-06-17). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used. |
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| Model | WER | CER | |
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| ------------- | ------------- | ------------- | |
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| jonatasgrosman/wav2vec2-large-xlsr-53-english | **18.98%** | **8.29%** | |
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| jonatasgrosman/wav2vec2-large-english | 21.53% | 9.66% | |
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| facebook/wav2vec2-large-960h-lv60-self | 22.03% | 10.39% | |
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| facebook/wav2vec2-large-960h-lv60 | 23.97% | 11.14% | |
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| boris/xlsr-en-punctuation | 29.10% | 10.75% | |
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| facebook/wav2vec2-large-960h | 32.79% | 16.03% | |
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| facebook/wav2vec2-base-960h | 39.86% | 19.89% | |
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| facebook/wav2vec2-base-100h | 51.06% | 25.06% | |
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| elgeish/wav2vec2-large-lv60-timit-asr | 59.96% | 34.28% | |
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| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 66.41% | 36.76% | |
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| elgeish/wav2vec2-base-timit-asr | 68.78% | 36.81% | |
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