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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: 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: 21.53 |
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- name: Test CER |
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type: cer |
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value: 9.66 |
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--- |
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# Wav2vec2-Large-English |
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Fine-tuned [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) 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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## Usage |
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The model can be used directly (without a language model) as follows... |
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Using the [ASRecognition](https://github.com/jonatasgrosman/asrecognition) library: |
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```python |
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from asrecognition import ASREngine |
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asr = ASREngine("fr", model_path="jonatasgrosman/wav2vec2-large-english") |
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audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"] |
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transcriptions = asr.transcribe(audio_paths) |
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``` |
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Writing your own inference script: |
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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-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." | SHELL BE ALL RIGHT | |
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| SIX | SIX | |
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| "ALL'S WELL THAT ENDS WELL." | ALLAS WELL THAT ENDS WELL | |
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| DO YOU MEAN IT? | W 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 REGRESTION | |
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| HOW IS MOZILLA GOING TO HANDLE AMBIGUITIES LIKE QUEUE AND CUE? | HOW IS MOSILLA GOING TO BANDL AND BE WHIT IS LIKE QU AND QU | |
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| "I GUESS YOU MUST THINK I'M KINDA BATTY." | RUSTION AS HAME AK AN THE POT | |
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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 GUCE 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 (en) 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-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. 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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| wav2vec2-large-xlsr-53-english | **18.98%** | **8.29%** | |
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| wav2vec2-large-xlsr-hindi | 20.01% | 9.66% | |
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| wav2vec2-large-english | 22.00% | 9.66% | |
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| wav2vec2-large-960h-lv60-english | 22.03% | 10.39% | |
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| wav2vec2-base-100h-lv60-english | 24.97% | 11.14% | |
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