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Model: jonatasgrosman/wav2vec2-large-xlsr-53-japanese Source: Original Platform
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README.md
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README.md
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---
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language: ja
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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 Japanese 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 ja
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type: common_voice
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args: ja
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metrics:
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- name: Test WER
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type: wer
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value: 81.80
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- name: Test CER
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type: cer
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value: 20.16
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---
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# Fine-tuned XLSR-53 large model for speech recognition in Japanese
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Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the train and validation splits of [Common Voice 6.1](https://huggingface.co/datasets/common_voice), [CSS10](https://github.com/Kyubyong/css10) and [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut).
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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 [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
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```python
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from huggingsound import SpeechRecognitionModel
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model = SpeechRecognitionModel("jonatasgrosman/wav2vec2-large-xlsr-53-japanese")
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audio_paths = ["/path/to/file.mp3", "/path/to/another_file.wav"]
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transcriptions = model.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 = "ja"
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese"
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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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| 祖母は、おおむね機嫌よく、サイコロをころがしている。 | 人母は重にきね起くさいがしている |
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| 財布をなくしたので、交番へ行きます。 | 財布をなく手端ので勾番へ行きます |
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| 飲み屋のおやじ、旅館の主人、医者をはじめ、交際のある人にきいてまわったら、みんな、私より収入が多いはずなのに、税金は安い。 | ノ宮屋のお親じ旅館の主に医者をはじめ交際のアル人トに聞いて回ったらみんな私より収入が多いはなうに税金は安い |
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| 新しい靴をはいて出かけます。 | だらしい靴をはいて出かけます |
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| このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表現することがある | このためプラズマ中のイオンや電子の持つ平均運動エネルギーを温度で表弁することがある |
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| 松井さんはサッカーより野球のほうが上手です。 | 松井さんはサッカーより野球のほうが上手です |
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| 新しいお皿を使います。 | 新しいお皿を使います |
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| 結婚以来三年半ぶりの東京も、旧友とのお酒も、夜行列車も、駅で寝て、朝を待つのも久しぶりだ。 | 結婚ル二来三年半降りの東京も吸とのお酒も野越者も駅で寝て朝を待つの久しぶりた |
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| これまで、少年野球、ママさんバレーなど、地域スポーツを支え、市民に密着してきたのは、無数のボランティアだった。 | これまで少年野球<unk>三バレーなど地域スポーツを支え市民に満着してきたのは娘数のボランティアだった |
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| 靴を脱いで、スリッパをはきます。 | 靴を脱いでスイパーをはきます |
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## Evaluation
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The model can be evaluated as follows on the Japanese 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 = "ja"
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MODEL_ID = "jonatasgrosman/wav2vec2-large-xlsr-53-japanese"
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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 2021-05-10). 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-japanese | **81.80%** | **20.16%** |
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| vumichien/wav2vec2-large-xlsr-japanese | 1108.86% | 23.40% |
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| qqhann/w2v_hf_jsut_xlsr53 | 1012.18% | 70.77% |
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## Citation
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If you want to cite this model you can use this:
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```bibtex
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@misc{grosman2021xlsr53-large-japanese,
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title={Fine-tuned {XLSR}-53 large model for speech recognition in {J}apanese},
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author={Grosman, Jonatas},
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howpublished={\url{https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-japanese}},
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year={2021}
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}
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```
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