177 lines
6.9 KiB
Markdown
177 lines
6.9 KiB
Markdown
---
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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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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 = [",", "?", "¿", ".", "!", "¡", ";", "；", ":", '""', "%", '"', "<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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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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