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---
language: en
datasets:
- common_voice
metrics:
- wer
- cer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 English by Jonatas Grosman
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice en
type: common_voice
args: en
metrics:
- name: Test WER
type: wer
value: 39.59
- name: Test CER
type: cer
value: 18.18
---
# Wav2Vec2-Large-XLSR-53-English
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 ).
When using this model, make sure that your speech input is sampled at 16kHz.
The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import librosa
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "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)
# Preprocessing the datasets.
# 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 = 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)
```
| Reference | Prediction |
| ------------- | ------------- |
| "SHE'LL BE ALL RIGHT." | SHE'LD BE ALL RIGHT |
| SIX | SIX |
| "ALL'S WELL THAT ENDS WELL." | ALL IS WELL THAT ENDS WELL |
| DO YOU MEAN IT? | DO YOU MEAN IT |
| 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 |
| HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? | HOWIS MOCILE ARE GOING TO HANDLE AMBIGUITIES LIKE KU AND KU |
| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RISSHON WAS INCAN IN THE BAK TE |
| NO ONE NEAR THE REMOTE MACHINE YOU COULD RING? | NO ONE NEAR THE REMOTE MACHINE YOU COULD RING |
| SAUCE FOR THE GOOSE IS SAUCE FOR THE GANDER. | SAUCE FOR THE GUISE IS SAUCED FOR THE GONDER |
| GROVES STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD. | GRAFS STARTED WRITING SONGS WHEN SHE WAS FOUR YEARS OLD |
## Evaluation
The model can be evaluated as follows on the English test data of Common Voice.
```python
import torch
import re
import librosa
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
LANG_ID = "en"
MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-english"
DEVICE = "cuda"
CHARS_TO_IGNORE = [",", "?", "¿", ".", "!", "¡", ";", "； <22> ", "ʿ", "·", "჻", "~", "՞",
"؟", "،", "।", "॥", "«", "»", "„", "“", "”", "「", "」", "‘ ’
"{", "}", "=", "`", "_", "+", "< ", ">", "…", "– ´ ʾ ‹ ›
"、", "﹂", "﹁", "‧", "～ ﹏ ， ｛ ｝ （ ） ［ ］
"『", "』", "〝", "〟", "⟨", "⟩", "〜", "： ！ ？ − ʻ ˆ
test_dataset = load_dataset("common_voice", LANG_ID, split="test")
# uncomment the following lines to eval using other datasets
# test_dataset = load_dataset("librispeech_asr", "clean", split="test")
# test_dataset = load_dataset("librispeech_asr", "other", split="test")
# test_dataset = load_dataset("timit_asr", split="test")
wer = load_metric("wer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/wer.py
cer = load_metric("cer.py") # https://github.com/jonatasgrosman/wav2vec2-sprint/blob/main/cer.py
chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
model.to(DEVICE)
# Preprocessing the datasets.
# We need to read the audio files as arrays
def speech_file_to_array_fn(batch):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
speech_array, sampling_rate = librosa.load(batch["file"] if "file" in batch else batch["path"], sr=16_000)
batch["speech"] = speech_array
batch["sentence"] = re.sub(chars_to_ignore_regex, "", batch["text"] if "text" in batch else batch["sentence"]).upper()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the audio files as arrays
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(DEVICE), attention_mask=inputs.attention_mask.to(DEVICE)).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)
predictions = [x.upper() for x in result["pred_strings"]]
references = [x.upper() for x in result["sentence"]]
print(f"WER: {wer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
print(f"CER: {cer.compute(predictions=predictions, references=references, chunk_size=1000) * 100}")
```
**Test Result**:
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.
---
**Common Voice**
| Model | WER | CER |
| ------------- | ------------- | ------------- |
| jonatasgrosman/wav2vec2-large-xlsr-53-english | **19.18%** | **8.25%** |
| jonatasgrosman/wav2vec2-large-english | 21.16% | 9.53% |
| facebook/wav2vec2-large-960h-lv60-self | 22.03% | 10.39% |
| facebook/wav2vec2-large-960h-lv60 | 23.97% | 11.14% |
| facebook/wav2vec2-large-960h | 32.79% | 16.03% |
| boris/xlsr-en-punctuation | 34.81% | 15.51% |
| facebook/wav2vec2-base-960h | 39.86% | 19.89% |
| facebook/wav2vec2-base-100h | 51.06% | 25.06% |
| elgeish/wav2vec2-large-lv60-timit-asr | 59.96% | 34.28% |
| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 66.41% | 36.76% |
| elgeish/wav2vec2-base-timit-asr | 68.78% | 36.81% |
---
**LibriSpeech (clean)**
| Model | WER | CER |
| ------------- | ------------- | ------------- |
| facebook/wav2vec2-large-960h-lv60-self | **1.86%** | **0.54%** |
| facebook/wav2vec2-large-960h-lv60 | 2.15% | 0.61% |
| facebook/wav2vec2-large-960h | 2.82% | 0.84% |
| facebook/wav2vec2-base-960h | 3.44% | 1.06% |
| facebook/wav2vec2-base-100h | 6.26% | 2.00% |
| jonatasgrosman/wav2vec2-large-xlsr-53-english | 6.97% | 2.02% |
| jonatasgrosman/wav2vec2-large-english | 8.00% | 2.55% |
| elgeish/wav2vec2-large-lv60-timit-asr | 15.53% | 4.93% |
| boris/xlsr-en-punctuation | 19.28% | 6.45% |
| elgeish/wav2vec2-base-timit-asr | 29.19% | 8.38% |
| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 31.82% | 12.41% |
---
**LibriSpeech (other)**
| Model | WER | CER |
| ------------- | ------------- | ------------- |
| facebook/wav2vec2-large-960h-lv60-self | **3.89%** | **1.40%** |
| facebook/wav2vec2-large-960h-lv60 | 4.45% | 1.56% |
| facebook/wav2vec2-large-960h | 6.49% | 2.52% |
| facebook/wav2vec2-base-960h | 8.90% | 3.55% |
| jonatasgrosman/wav2vec2-large-xlsr-53-english | 11.75% | 4.23% |
| jonatasgrosman/wav2vec2-large-english | 13.62% | 5.24% |
| facebook/wav2vec2-base-100h | 13.97% | 5.51% |
| boris/xlsr-en-punctuation | 26.40% | 10.11% |
| elgeish/wav2vec2-large-lv60-timit-asr | 28.39% | 12.08% |
| elgeish/wav2vec2-base-timit-asr | 42.04% | 15.57% |
| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 45.19% | 20.32% |
---
**TIMIT**
| Model | WER | CER |
| ------------- | ------------- | ------------- |
| facebook/wav2vec2-large-960h-lv60-self | **5.17%** | **1.33%** |
| facebook/wav2vec2-large-960h-lv60 | 6.24% | 1.54% |
| facebook/wav2vec2-large-960h | 9.63% | 2.19% |
| facebook/wav2vec2-base-960h | 11.48% | 2.76% |
| jonatasgrosman/wav2vec2-large-xlsr-53-english | 11.93% | 3.50% |
| elgeish/wav2vec2-large-lv60-timit-asr | 13.83% | 4.36% |
| jonatasgrosman/wav2vec2-large-english | 13.91% | 4.01% |
| facebook/wav2vec2-base-100h | 16.75% | 4.79% |
| elgeish/wav2vec2-base-timit-asr | 25.40% | 8.16% |
| boris/xlsr-en-punctuation | 25.93% | 9.99% |
| facebook/wav2vec2-base-10k-voxpopuli-ft-en | 51.08% | 19.84% |
Jonatas Grosman