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This commit is contained in:
Jonatas Grosman
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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: 39.59
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- name: Test CER
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type: cer
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value: 18.18
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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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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'LD BE ALL RIGHT |
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| SIX | SIX |
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| "ALL'S WELL THAT ENDS WELL." | ALL IS 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? | HOWIS MOCILE ARE GOING TO HANDLE AMBIGUITIES LIKE KU AND KU |
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| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RISSHON WAS INCAN IN THE BAK TE |
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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 GUISE IS SAUCED 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 = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "<22>", "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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# uncomment the following lines to eval using other datasets
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# test_dataset = load_dataset("librispeech_asr", "clean", split="test")
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# test_dataset = load_dataset("librispeech_asr", "other", split="test")
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# test_dataset = load_dataset("timit_asr", 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["file"] if "file" in batch else 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["text"] if "text" in batch else 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 2021-05-20). 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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---
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**Common Voice**
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| Model | WER | CER |
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| ------------- | ------------- | ------------- |
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| jonatasgrosman/wav2vec2-large-xlsr-53-english | **19.18%** | **8.25%** |
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| jonatasgrosman/wav2vec2-large-english | 21.16% | 9.53% |
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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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| facebook/wav2vec2-large-960h | 32.79% | 16.03% |
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| boris/xlsr-en-punctuation | 34.81% | 15.51% |
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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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---
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**LibriSpeech (clean)**
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| Model | WER | CER |
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| ------------- | ------------- | ------------- |
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| facebook/wav2vec2-large-960h-lv60-self | **1.86%** | **0.54%** |
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| facebook/wav2vec2-large-960h-lv60 | 2.15% | 0.61% |
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| facebook/wav2vec2-large-960h | 2.82% | 0.84% |
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| facebook/wav2vec2-base-960h | 3.44% | 1.06% |
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| facebook/wav2vec2-base-100h | 6.26% | 2.00% |
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| jonatasgrosman/wav2vec2-large-xlsr-53-english | 6.97% | 2.02% |
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| jonatasgrosman/wav2vec2-large-english | 8.00% | 2.55% |
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| elgeish/wav2vec2-large-lv60-timit-asr | 15.53% | 4.93% |
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| boris/xlsr-en-punctuation | 19.28% | 6.45% |
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| elgeish/wav2vec2-base-timit-asr | 29.19% | 8.38% |
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| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 31.82% | 12.41% |
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---
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**LibriSpeech (other)**
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| Model | WER | CER |
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| ------------- | ------------- | ------------- |
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| facebook/wav2vec2-large-960h-lv60-self | **3.89%** | **1.40%** |
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| facebook/wav2vec2-large-960h-lv60 | 4.45% | 1.56% |
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| facebook/wav2vec2-large-960h | 6.49% | 2.52% |
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| facebook/wav2vec2-base-960h | 8.90% | 3.55% |
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| jonatasgrosman/wav2vec2-large-xlsr-53-english | 11.75% | 4.23% |
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| jonatasgrosman/wav2vec2-large-english | 13.62% | 5.24% |
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| facebook/wav2vec2-base-100h | 13.97% | 5.51% |
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| boris/xlsr-en-punctuation | 26.40% | 10.11% |
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| elgeish/wav2vec2-large-lv60-timit-asr | 28.39% | 12.08% |
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| elgeish/wav2vec2-base-timit-asr | 42.04% | 15.57% |
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| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 45.19% | 20.32% |
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---
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**TIMIT**
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| Model | WER | CER |
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| ------------- | ------------- | ------------- |
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| facebook/wav2vec2-large-960h-lv60-self | **5.17%** | **1.33%** |
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| facebook/wav2vec2-large-960h-lv60 | 6.24% | 1.54% |
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| facebook/wav2vec2-large-960h | 9.63% | 2.19% |
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| facebook/wav2vec2-base-960h | 11.48% | 2.76% |
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| jonatasgrosman/wav2vec2-large-xlsr-53-english | 11.93% | 3.50% |
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| elgeish/wav2vec2-large-lv60-timit-asr | 13.83% | 4.36% |
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| jonatasgrosman/wav2vec2-large-english | 13.91% | 4.01% |
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| facebook/wav2vec2-base-100h | 16.75% | 4.79% |
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| elgeish/wav2vec2-base-timit-asr | 25.40% | 8.16% |
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| boris/xlsr-en-punctuation | 25.93% | 9.99% |
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| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 51.08% | 19.84% |
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@ -0,0 +1,76 @@
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{
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"_name_or_path": "facebook/wav2vec2-large-xlsr-53",
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"activation_dropout": 0.05,
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"apply_spec_augment": true,
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"architectures": [
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"Wav2Vec2ForCTC"
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],
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"attention_dropout": 0.1,
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"bos_token_id": 1,
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"conv_bias": true,
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"conv_dim": [
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512,
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512,
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512,
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512,
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512,
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512,
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512
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],
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"conv_kernel": [
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10,
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3,
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3,
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3,
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3,
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2,
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2
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],
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"conv_stride": [
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5,
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2,
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2,
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2,
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2,
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2,
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2
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],
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"ctc_loss_reduction": "mean",
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"ctc_zero_infinity": true,
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"do_stable_layer_norm": true,
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"eos_token_id": 2,
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"feat_extract_activation": "gelu",
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"feat_extract_dropout": 0.0,
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"feat_extract_norm": "layer",
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"feat_proj_dropout": 0.05,
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"final_dropout": 0.0,
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"gradient_checkpointing": true,
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"hidden_act": "gelu",
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"hidden_dropout": 0.05,
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 4096,
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"layer_norm_eps": 1e-05,
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"layerdrop": 0.05,
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"mask_channel_length": 10,
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"mask_channel_min_space": 1,
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"mask_channel_other": 0.0,
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"mask_channel_prob": 0.0,
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"mask_channel_selection": "static",
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"mask_feature_length": 10,
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"mask_feature_prob": 0.0,
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"mask_time_length": 10,
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"mask_time_min_space": 1,
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"mask_time_other": 0.0,
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"mask_time_prob": 0.05,
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"mask_time_selection": "static",
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"model_type": "wav2vec2",
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"num_attention_heads": 16,
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"num_conv_pos_embedding_groups": 16,
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"num_conv_pos_embeddings": 128,
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"num_feat_extract_layers": 7,
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"num_hidden_layers": 24,
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"pad_token_id": 0,
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"transformers_version": "4.5.0.dev0",
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"vocab_size": 33
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}
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@ -0,0 +1,8 @@
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{
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"do_normalize": true,
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"feature_size": 1,
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"padding_side": "right",
|
||||||
|
"padding_value": 0.0,
|
||||||
|
"return_attention_mask": true,
|
||||||
|
"sampling_rate": 16000
|
||||||
|
}
|
||||||
|
|
@ -0,0 +1,3 @@
|
||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:3ba6ad16f6ecfcadd07ca5bc3353c3168665bee9fbfb160fbc864a6a9f87ca58
|
||||||
|
size 1262069143
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
{"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
|
||||||
|
|
@ -0,0 +1 @@
|
||||||
|
{"<pad>": 0, "<s>": 1, "</s>": 2, "<unk>": 3, "|": 4, "'": 5, "-": 6, "A": 7, "B": 8, "C": 9, "D": 10, "E": 11, "F": 12, "G": 13, "H": 14, "I": 15, "J": 16, "K": 17, "L": 18, "M": 19, "N": 20, "O": 21, "P": 22, "Q": 23, "R": 24, "S": 25, "T": 26, "U": 27, "V": 28, "W": 29, "X": 30, "Y": 31, "Z": 32}
|
||||||
Loading…
Reference in New Issue