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README.md ADDED
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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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+
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+ # Fine-tuned XLSR-53 large model for speech recognition in Japanese
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+
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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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+
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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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+
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+ The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
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+
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+ ## Usage
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+
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+ The model can be used directly (without a language model) as follows...
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+
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+ Using the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) library:
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+
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+ ```python
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+ from huggingsound import SpeechRecognitionModel
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+
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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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+
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+ transcriptions = model.transcribe(audio_paths)
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+ ```
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+
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+ Writing your own inference script:
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+
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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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+
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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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+
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+ test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
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+
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+ processor = Wav2Vec2Processor.from_pretrained(MODEL_ID)
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+ model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ ## Evaluation
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+
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+ The model can be evaluated as follows on the Japanese test data of Common Voice.
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+
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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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+
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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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+
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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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+
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+ test_dataset = load_dataset("common_voice", LANG_ID, split="test")
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+
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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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+
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+ chars_to_ignore_regex = f"[{re.escape(''.join(CHARS_TO_IGNORE))}]"
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+
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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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+
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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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+
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+ test_dataset = test_dataset.map(speech_file_to_array_fn)
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+
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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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+
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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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+
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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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+
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+ result = test_dataset.map(evaluate, batched=True, batch_size=8)
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+
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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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+
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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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+
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+ **Test Result**:
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+
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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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+
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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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+
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+ ## Citation
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+ If you want to cite this model you can use this:
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+
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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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509, "喰": 510, "営": 511, "嗅": 512, "嗜": 513, "嗟": 514, "嘆": 515, "嘘": 516, "嘩": 517, "嘲": 518, "噂": 519, "噌": 520, "器": 521, "噴": 522, "嚀": 523, "嚇": 524, "嚢": 525, "囚": 526, "四": 527, "回": 528, "因": 529, "団": 530, "困": 531, "囲": 532, "図": 533, "固": 534, "国": 535, "圏": 536, "園": 537, "土": 538, "圧": 539, "在": 540, "地": 541, "坂": 542, "均": 543, "坊": 544, "坐": 545, "坑": 546, "坦": 547, "坪": 548, "垂": 549, "型": 550, "埃": 551, "埋": 552, "城": 553, "埒": 554, "域": 555, "執": 556, "培": 557, "基": 558, "堀": 559, "堂": 560, "堅": 561, "堕": 562, "堤": 563, "堪": 564, "堰": 565, "報": 566, "場": 567, "堵": 568, "塊": 569, "塗": 570, "塚": 571, "塞": 572, "塩": 573, "境": 574, "墓": 575, "増": 576, "墜": 577, "壁": 578, "壊": 579, "士": 580, "壮": 581, "声": 582, "売": 583, "壺": 584, "変": 585, "夏": 586, "夕": 587, "外": 588, "多": 589, "夜": 590, "夢": 591, "大": 592, "天": 593, "太": 594, "夫": 595, "央": 596, "失": 597, "夷": 598, "奇": 599, "奉": 600, "奏": 601, "契": 602, "套": 603, "奥": 604, "奪": 605, "奮": 606, "女": 607, "奴": 608, "好": 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1190, "欧": 1191, "欲": 1192, "款": 1193, "歇": 1194, "歌": 1195, "歓": 1196, "止": 1197, "正": 1198, "此": 1199, "武": 1200, "歩": 1201, "歪": 1202, "歯": 1203, "歳": 1204, "歴": 1205, "死": 1206, "殊": 1207, "残": 1208, "殴": 1209, "段": 1210, "殺": 1211, "殻": 1212, "毀": 1213, "母": 1214, "毎": 1215, "毒": 1216, "比": 1217, "毛": 1218, "毫": 1219, "氏": 1220, "民": 1221, "気": 1222, "氣": 1223, "水": 1224, "氷": 1225, "永": 1226, "求": 1227, "汗": 1228, "汚": 1229, "江": 1230, "池": 1231, "汰": 1232, "汲": 1233, "決": 1234, "汽": 1235, "沃": 1236, "沈": 1237, "沓": 1238, "沖": 1239, "沙": 1240, "没": 1241, "沢": 1242, "河": 1243, "沸": 1244, "油": 1245, "治": 1246, "沿": 1247, "況": 1248, "泉": 1249, "泊": 1250, "法": 1251, "泡": 1252, "波": 1253, "泣": 1254, "泥": 1255, "注": 1256, "泳": 1257, "洋": 1258, "洒": 1259, "洗": 1260, "洞": 1261, "津": 1262, "洩": 1263, "洪": 1264, "活": 1265, "派": 1266, "流": 1267, "浄": 1268, "浅": 1269, "浦": 1270, "浪": 1271, "浮": 1272, "浴": 1273, "海": 1274, "浸": 1275, "消": 1276, "涙": 1277, "液": 1278, "涼": 1279, "淋": 1280, "淡": 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1463, "痢": 1464, "痩": 1465, "瘍": 1466, "療": 1467, "癇": 1468, "癌": 1469, "癒": 1470, "癖": 1471, "発": 1472, "登": 1473, "白": 1474, "百": 1475, "的": 1476, "皆": 1477, "皇": 1478, "皮": 1479, "皺": 1480, "皿": 1481, "盃": 1482, "盆": 1483, "盗": 1484, "盛": 1485, "盟": 1486, "監": 1487, "盥": 1488, "盪": 1489, "目": 1490, "盲": 1491, "直": 1492, "相": 1493, "盾": 1494, "省": 1495, "眉": 1496, "看": 1497, "県": 1498, "真": 1499, "眠": 1500, "眸": 1501, "眺": 1502, "眼": 1503, "着": 1504, "睡": 1505, "睦": 1506, "睫": 1507, "瞥": 1508, "瞬": 1509, "矛": 1510, "矢": 1511, "知": 1512, "短": 1513, "矯": 1514, "石": 1515, "砂": 1516, "研": 1517, "砕": 1518, "砲": 1519, "破": 1520, "硝": 1521, "硫": 1522, "硬": 1523, "碁": 1524, "碑": 1525, "確": 1526, "磨": 1527, "礁": 1528, "礎": 1529, "示": 1530, "礼": 1531, "社": 1532, "祖": 1533, "祝": 1534, "神": 1535, "祟": 1536, "票": 1537, "禁": 1538, "禎": 1539, "福": 1540, "禰": 1541, "秀": 1542, "私": 1543, "秋": 1544, "科": 1545, "秒": 1546, "秘": 1547, "租": 1548, "称": 1549, "移": 1550, "程": 1551, "税": 1552, "稚": 1553, "種": 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1645, "絶": 1646, "絹": 1647, "継": 1648, "続": 1649, "綜": 1650, "綱": 1651, "網": 1652, "綺": 1653, "綻": 1654, "綾": 1655, "綿": 1656, "緊": 1657, "総": 1658, "緑": 1659, "緒": 1660, "線": 1661, "締": 1662, "編": 1663, "緩": 1664, "緯": 1665, "練": 1666, "縁": 1667, "縄": 1668, "縊": 1669, "縞": 1670, "縦": 1671, "縮": 1672, "繁": 1673, "繊": 1674, "繋": 1675, "繍": 1676, "織": 1677, "繕": 1678, "繭": 1679, "繰": 1680, "纏": 1681, "罎": 1682, "罠": 1683, "罪": 1684, "置": 1685, "罰": 1686, "署": 1687, "罵": 1688, "罹": 1689, "羊": 1690, "美": 1691, "群": 1692, "義": 1693, "羽": 1694, "翅": 1695, "翌": 1696, "習": 1697, "翰": 1698, "翳": 1699, "翻": 1700, "翼": 1701, "老": 1702, "考": 1703, "者": 1704, "耐": 1705, "耳": 1706, "聖": 1707, "聞": 1708, "聯": 1709, "聳": 1710, "聴": 1711, "職": 1712, "肉": 1713, "肖": 1714, "肚": 1715, "肛": 1716, "肝": 1717, "股": 1718, "肥": 1719, "肩": 1720, "肯": 1721, "肱": 1722, "育": 1723, "肴": 1724, "胃": 1725, "胆": 1726, "背": 1727, "胞": 1728, "胡": 1729, "胥": 1730, "胴": 1731, "胸": 1732, "能": 1733, "脅": 1734, "脇": 1735, "脈": 1736, "脚": 1737, "脱": 1738, "脳": 1739, "腐": 1740, "腑": 1741, "腔": 1742, "腕": 1743, "腫": 1744, "腮": 1745, "腰": 1746, "腹": 1747, "腺": 1748, "膏": 1749, "膚": 1750, "膜": 1751, "膝": 1752, "膣": 1753, "膨": 1754, "膳": 1755, "臆": 1756, "臍": 1757, "臓": 1758, "臥": 1759, "臨": 1760, "自": 1761, "臭": 1762, "至": 1763, "致": 1764, "興": 1765, "舌": 1766, "舎": 1767, "舗": 1768, "舞": 1769, "航": 1770, "般": 1771, "船": 1772, "艦": 1773, "良": 1774, "色": 1775, "芝": 1776, "芯": 1777, "花": 1778, "芸": 1779, "芽": 1780, "苔": 1781, "苗": 1782, "若": 1783, "苦": 1784, "英": 1785, "茂": 1786, "茎": 1787, "茨": 1788, "茶": 1789, "茸": 1790, "草": 1791, "荒": 1792, "荘": 1793, "荷": 1794, "菊": 1795, "菌": 1796, "菓": 1797, "菜": 1798, "菩": 1799, "華": 1800, "落": 1801, "葉": 1802, "著": 1803, "葢": 1804, "葬": 1805, "蒲": 1806, "蒸": 1807, "蒼": 1808, "蓄": 1809, "蔑": 1810, "蔭": 1811, "蔵": 1812, "薄": 1813, "薩": 1814, "薪": 1815, "薬": 1816, "藤": 1817, "藩": 1818, "藪": 1819, "蘇": 1820, "蘭": 1821, "虎": 1822, "虐": 1823, "虚": 1824, "虫": 1825, "蚕": 1826, "蛇": 1827, "蛛": 1828, "蜘": 1829, "蝦": 1830, "融": 1831, "蟠": 1832, "蠅": 1833, "血": 1834, "衆": 1835, "行": 1836, "術": 1837, "街": 1838, "衛": 1839, "衝": 1840, "衣": 1841, "表": 1842, "袂": 1843, "袈": 1844, "袋": 1845, "袍": 1846, "袖": 1847, "袢": 1848, "被": 1849, "袴": 1850, "裁": 1851, "裂": 1852, "装": 1853, "裏": 1854, "裕": 1855, "補": 1856, "裟": 1857, "裡": 1858, "裸": 1859, "裹": 1860, "製": 1861, "褄": 1862, "複": 1863, "褞": 1864, "襖": 1865, "襞": 1866, "襟": 1867, "襦": 1868, "襲": 1869, "西": 1870, "要": 1871, "覆": 1872, "見": 1873, "規": 1874, "視": 1875, "覗": 1876, "覘": 1877, "覚": 1878, "覧": 1879, "親": 1880, "観": 1881, "角": 1882, "解": 1883, "触": 1884, "言": 1885, "訂": 1886, "計": 1887, "訊": 1888, "討": 1889, "訓": 1890, "託": 1891, "記": 1892, "訪": 1893, "設": 1894, "許": 1895, "訳": 1896, "訴": 1897, "診": 1898, "証": 1899, "評": 1900, "詞": 1901, "試": 1902, "詩": 1903, "詫": 1904, "詮": 1905, "詰": 1906, "話": 1907, "該": 1908, "詳": 1909, "誂": 1910, "誇": 1911, "誉": 1912, "誌": 1913, "認": 1914, "誓": 1915, "誕": 1916, "誘": 1917, "語": 1918, "誠": 1919, "誤": 1920, "説": 1921, "読": 1922, "誰": 1923, "課": 1924, "誼": 1925, "調": 1926, "談": 1927, "請": 1928, "論": 1929, "諦": 1930, "諮": 1931, "諸": 1932, "諾": 1933, "謀": 1934, "謁": 1935, "謄": 1936, "謎": 1937, "謙": 1938, "謝": 1939, "謡": 1940, "識": 1941, "譜": 1942, "警": 1943, "議": 1944, "譲": 1945, "護": 1946, "讃": 1947, "谷": 1948, "谿": 1949, "豊": 1950, "豚": 1951, "象": 1952, "豹": 1953, "貌": 1954, "負": 1955, "財": 1956, "貢": 1957, "貧": 1958, "貨": 1959, "販": 1960, "貪": 1961, "貫": 1962, "責": 1963, "貯": 1964, "貰": 1965, "貴": 1966, "買": 1967, "貸": 1968, "費": 1969, "貿": 1970, "賀": 1971, "賂": 1972, "賃": 1973, "賄": 1974, "資": 1975, "賑": 1976, "賓": 1977, "賛": 1978, "賜": 1979, "賞": 1980, "賠": 1981, "賢": 1982, "賤": 1983, "質": 1984, "賭": 1985, "賺": 1986, "購": 1987, "贅": 1988, "贈": 1989, "贔": 1990, "赤": 1991, "赦": 1992, "赧": 1993, "赫": 1994, "走": 1995, "赴": 1996, "起": 1997, "超": 1998, "越": 1999, "趣": 2000, "足": 2001, "跋": 2002, "跚": 2003, "距": 2004, "跟": 2005, "跡": 2006, "跨": 2007, "路": 2008, "跳": 2009, "践": 2010, "踊": 2011, "踏": 2012, "蹣": 2013, "蹴": 2014, "躇": 2015, "躊": 2016, "身": 2017, "車": 2018, "軍": 2019, "軒": 2020, "軟": 2021, "転": 2022, "軽": 2023, "較": 2024, "載": 2025, "輝": 2026, "輩": 2027, "輪": 2028, "輯": 2029, "輸": 2030, "辛": 2031, "辞": 2032, "農": 2033, "辺": 2034, "辻": 2035, "込": 2036, "辿": 2037, "迎": 2038, "近": 2039, "返": 2040, "迦": 2041, "迫": 2042, "迭": 2043, "述": 2044, "迷": 2045, "迸": 2046, "迹": 2047, "追": 2048, "退": 2049, "送": 2050, "逃": 2051, "逅": 2052, "逆": 2053, "透": 2054, "途": 2055, "通": 2056, "逝": 2057, "速": 2058, "造": 2059, "逢": 2060, "連": 2061, "逮": 2062, "週": 2063, "進": 2064, "逸": 2065, "逼": 2066, "遂": 2067, "遅": 2068, "遇": 2069, "遊": 2070, "運": 2071, "遍": 2072, "過": 2073, "道": 2074, "達": 2075, "違": 2076, "遜": 2077, "遠": 2078, "遣": 2079, "遥": 2080, "適": 2081, "遮": 2082, "選": 2083, "遺": 2084, "避": 2085, "邂": 2086, "還": 2087, "那": 2088, "邦": 2089, "邪": 2090, "邸": 2091, "郊": 2092, "郎": 2093, "郡": 2094, "部": 2095, "郵": 2096, "郷": 2097, "都": 2098, "鄙": 2099, "鄭": 2100, "酌": 2101, "配": 2102, "酎": 2103, "酒": 2104, "酔": 2105, "酢": 2106, "酪": 2107, "酬": 2108, "酷": 2109, "酸": 2110, "醒": 2111, "醜": 2112, "醸": 2113, "釈": 2114, "里": 2115, "重": 2116, "野": 2117, "量": 2118, "金": 2119, "針": 2120, "釣": 2121, "鈴": 2122, "鉄": 2123, "鉛": 2124, "鉢": 2125, "鉤": 2126, "鉦": 2127, "鉱": 2128, "銀": 2129, "銃": 2130, "銅": 2131, "銘": 2132, "銭": 2133, "鋏": 2134, "鋒": 2135, "鋭": 2136, "鋳": 2137, "鋼": 2138, "錐": 2139, "錠": 2140, "錬": 2141, "錯": 2142, "録": 2143, "鍛": 2144, "鍵": 2145, "鎖": 2146, "鎮": 2147, "鏡": 2148, "鐉": 2149, "鐘": 2150, "鑑": 2151, "長": 2152, "門": 2153, "閃": 2154, "閉": 2155, "開": 2156, "閑": 2157, "間": 2158, "関": 2159, "閥": 2160, "闇": 2161, "闘": 2162, "阜": 2163, "阪": 2164, "防": 2165, "阿": 2166, "陀": 2167, "附": 2168, "陋": 2169, "降": 2170, "限": 2171, "院": 2172, "陣": 2173, "除": 2174, "陥": 2175, "陰": 2176, "陳": 2177, "陶": 2178, "陸": 2179, "険": 2180, "陽": 2181, "隅": 2182, "隊": 2183, "階": 2184, "随": 2185, "隔": 2186, "隙": 2187, "際": 2188, "障": 2189, "隠": 2190, "隣": 2191, "隻": 2192, "雀": 2193, "雁": 2194, "雄": 2195, "雅": 2196, "集": 2197, "雇": 2198, "雑": 2199, "離": 2200, "難": 2201, "雨": 2202, "雪": 2203, "雲": 2204, "零": 2205, "雷": 2206, "電": 2207, "震": 2208, "霊": 2209, "霜": 2210, "霞": 2211, "霧": 2212, "露": 2213, "靄": 2214, "青": 2215, "靖": 2216, "静": 2217, "非": 2218, "靠": 2219, "面": 2220, "革": 2221, "靴": 2222, "鞘": 2223, "鞭": 2224, "音": 2225, "響": 2226, "頁": 2227, "頂": 2228, "頃": 2229, "項": 2230, "順": 2231, "須": 2232, "預": 2233, "頑": 2234, "頒": 2235, "頓": 2236, "領": 2237, "頚": 2238, "頬": 2239, "頭": 2240, "頷": 2241, "頼": 2242, "題": 2243, "額": 2244, "顔": 2245, "顕": 2246, "願": 2247, "類": 2248, "顧": 2249, "顫": 2250, "顰": 2251, "風": 2252, "飄": 2253, "飛": 2254, "食": 2255, "飢": 2256, "飭": 2257, "飯": 2258, "飲": 2259, "飼": 2260, "飽": 2261, "飾": 2262, "養": 2263, "餌": 2264, "餞": 2265, "館": 2266, "饒": 2267, "首": 2268, "香": 2269, "馬": 2270, "馳": 2271, "馴": 2272, "駄": 2273, "駅": 2274, "駆": 2275, "駈": 2276, "駐": 2277, "駒": 2278, "騎": 2279, "騒": 2280, "験": 2281, "騙": 2282, "騰": 2283, "驚": 2284, "骨": 2285, "髄": 2286, "高": 2287, "髣": 2288, "髪": 2289, "髭": 2290, "髯": 2291, "髴": 2292, "髷": 2293, "鬘": 2294, "鬼": 2295, "魂": 2296, "魅": 2297, "魔": 2298, "魚": 2299, "鮮": 2300, "鯨": 2301, "鰻": 2302, "鱈": 2303, "鱒": 2304, "鱗": 2305, "鳥": 2306, "鳩": 2307, "鳴": 2308, "鳶": 2309, "鵜": 2310, "鶏": 2311, "鹿": 2312, "麒": 2313, "麓": 2314, "麗": 2315, "麟": 2316, "麦": 2317, "麭": 2318, "麺": 2319, "麻": 2320, "黄": 2321, "黒": 2322, "黙": 2323, "鼓": 2324, "鼠": 2325, "鼻": 2326, "齎": 2327, "齢": 2328, "龍": 2329, "0": 2330, "1": 2331, "2": 2332, "3": 2333, "4": 2334, "5": 2335, "6": 2336, "7": 2337, "8": 2338, "9": 2339, "I": 2340}