153 lines
4.9 KiB
Markdown
153 lines
4.9 KiB
Markdown
---
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language: en
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datasets:
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- common_voice
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- mozilla-foundation/common_voice_6_0
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metrics:
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- wer
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- cer
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tags:
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- en
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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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- robust-speech-event
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- mozilla-foundation/common_voice_6_0
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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: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Common Voice pt
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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: 19.06
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- name: Test CER
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type: cer
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value: 7.69
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- name: Test WER (+LM)
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type: wer
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value: 14.81
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- name: Test CER (+LM)
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type: cer
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value: 6.84
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: Robust Speech Event - Dev Data
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type: speech-recognition-community-v2/dev_data
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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: 27.72
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- name: Test CER
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type: cer
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value: 11.65
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- name: Test WER (+LM)
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type: wer
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value: 20.85
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- name: Test CER (+LM)
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type: cer
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value: 11.01
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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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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("en", model_path="jonatasgrosman/wav2vec2-large-xlsr-53-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-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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1. To evaluate on `mozilla-foundation/common_voice_6_0` with split `test`
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```bash
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python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-english --dataset mozilla-foundation/common_voice_6_0 --config en --split test
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```
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2. To evaluate on `speech-recognition-community-v2/dev_data`
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```bash
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python eval.py --model_id jonatasgrosman/wav2vec2-large-xlsr-53-english --dataset speech-recognition-community-v2/dev_data --config en --split validation --chunk_length_s 5.0 --stride_length_s 1.0
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```
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