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Model: vumichien/wav2vec2-large-xlsr-japanese
Source: Original Platform

README.md

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+---
+language: ja
+datasets:
+- common_voice
+metrics:
+- wer
+tags:
+- audio
+- automatic-speech-recognition
+- speech
+- xlsr-fine-tuning-week
+license: apache-2.0
+model-index:
+- name: XLSR Wav2Vec2 Japanese by Chien Vu
+  results:
+  - task:
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Common Voice Japanese
+      type: common_voice
+      args: ja
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 30.84
+       - name: Test CER
+         type: cer
+         value: 17.85
+widget:
+- example_title: Japanese speech corpus sample 1
+  src: https://u.pcloud.link/publink/show?code=XZwhAlXZFOtXiqKHMzmYS9wXrCP8Yb7EtRd7
+- example_title: Japanese speech corpus sample 2
+  src: https://u.pcloud.link/publink/show?code=XZ6hAlXZ5ccULt0YtrhJFl7LygKg0SJzKX0k
+---
+# Wav2Vec2-Large-XLSR-53-Japanese
+Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Japanese using the [Common Voice](https://huggingface.co/datasets/common_voice) and Japanese speech corpus of Saruwatari-lab, University of Tokyo [JSUT](https://sites.google.com/site/shinnosuketakamichi/publication/jsut).
+When using this model, make sure that your speech input is sampled at 16kHz.
+## Usage
+The model can be used directly (without a language model) as follows:
+```python
+!pip install mecab-python3
+!pip install unidic-lite
+!python -m unidic download
+import torch
+import torchaudio
+import librosa
+from datasets import load_dataset
+import MeCab
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+
+# config
+wakati = MeCab.Tagger("-Owakati")
+chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\、\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\。\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\．\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\「\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\」\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\…\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\？\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\・]'
+
+# load data, processor and model
+test_dataset = load_dataset("common_voice", "ja", split="test[:2%]")
+processor = Wav2Vec2Processor.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese")
+model = Wav2Vec2ForCTC.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese")
+resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000)
+
+# Preprocessing the datasets.
+def speech_file_to_array_fn(batch):
+    batch["sentence"] = wakati.parse(batch["sentence"]).strip()
+    batch["sentence"] = re.sub(chars_to_ignore_regex,'', batch["sentence"]).strip()
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler(sampling_rate, speech_array).squeeze()
+    return batch
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
+with torch.no_grad():
+    logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
+predicted_ids = torch.argmax(logits, dim=-1)
+print("Prediction:", processor.batch_decode(predicted_ids))
+print("Reference:", test_dataset["sentence"][:2])
+```
+## Evaluation
+The model can be evaluated as follows on the Japanese test data of Common Voice.
+```python
+!pip install mecab-python3
+!pip install unidic-lite
+!python -m unidic download
+
+import torch
+import librosa
+import torchaudio
+from datasets import load_dataset, load_metric
+import MeCab
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
+import re
+
+#config
+wakati = MeCab.Tagger("-Owakati")
+chars_to_ignore_regex = '[\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\、\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\。\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\．\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\「\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\」\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\…\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\？\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\・]'
+
+# load data, processor and model
+test_dataset = load_dataset("common_voice", "ja", split="test")
+wer = load_metric("wer")
+processor = Wav2Vec2Processor.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese")
+model = Wav2Vec2ForCTC.from_pretrained("vumichien/wav2vec2-large-xlsr-japanese")
+model.to("cuda")
+resampler = lambda sr, y: librosa.resample(y.numpy().squeeze(), sr, 16_000)
+
+# Preprocessing the datasets.
+def speech_file_to_array_fn(batch):
+    batch["sentence"] = wakati.parse(batch["sentence"]).strip()
+    batch["sentence"] = re.sub(chars_to_ignore_regex,'', batch["sentence"]).strip()
+    speech_array, sampling_rate = torchaudio.load(batch["path"])
+    batch["speech"] = resampler(sampling_rate, speech_array).squeeze()
+    return batch
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+
+# evaluate function
+def evaluate(batch):
+    inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
+    pred_ids = torch.argmax(logits, dim=-1)
+    batch["pred_strings"] = processor.batch_decode(pred_ids)
+    return batch
+result = test_dataset.map(evaluate, batched=True, batch_size=8)
+print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
+```
+## Test Result
+**WER:** 30.84%,
+**CER:** 17.85%
+
+## Training
+The Common Voice `train`, `validation` datasets and Japanese speech corpus `basic5000` datasets were used for training.
+