初始化项目，由ModelHub XC社区提供模型

Model: Ivydata/whisper-small-japanese
Source: Original Platform

README.md

@@ -0,0 +1,64 @@
+---
+license: apache-2.0
+datasets:
+- common_voice
+language:
+- ja
+tags:
+- audio
+---
+
+# Fine-tuned Japanese Whisper model for speech recognition using whisper-small
+Fine-tuned [openai/whisper-small](https://huggingface.co/openai/whisper-small) on Japanese using [Common Voice](https://commonvoice.mozilla.org/ja/datasets), [JVS](https://sites.google.com/site/shinnosuketakamichi/research-topics/jvs_corpus) and [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 as follows.
+```python
+from transformers import WhisperForConditionalGeneration, WhisperProcessor
+from datasets import load_dataset
+import librosa
+import torch
+
+LANG_ID = "ja"
+MODEL_ID = "Ivydata/whisper-small-japanese"
+SAMPLES = 10
+
+test_dataset = load_dataset("common_voice", LANG_ID, split=f"test[:{SAMPLES}]")
+processor = WhisperProcessor.from_pretrained(MODEL_ID)
+model = WhisperForConditionalGeneration.from_pretrained(MODEL_ID)
+model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(
+    language="ja", task="transcribe"
+)
+model.config.suppress_tokens = []
+
+# Preprocessing the datasets.
+# We need to read the audio files as arrays
+def speech_file_to_array_fn(batch):
+    speech_array, sampling_rate = librosa.load(batch["path"], sr=16_000)
+    batch["speech"] = speech_array
+    batch["sentence"] = batch["sentence"].upper()
+    batch["sampling_rate"] = sampling_rate
+    return batch
+
+test_dataset = test_dataset.map(speech_file_to_array_fn)
+sample = test_dataset[0]
+input_features = processor(sample["speech"], sampling_rate=sample["sampling_rate"], return_tensors="pt").input_features
+predicted_ids = model.generate(input_features)
+
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=False)
+# ['<|startoftranscript|><|ja|><|transcribe|><|notimestamps|>木村さんに電話を貸してもらいました。<|endoftext|>']
+
+transcription = processor.batch_decode(predicted_ids, skip_special_tokens=True)
+# ['木村さんに電話を貸してもらいました。']
+
+```
+
+## Test Result
+
+In the table below I report the Character Error Rate (CER) of the model tested on [TEDxJP-10K](https://github.com/laboroai/TEDxJP-10K) dataset.
+| Model | CER |
+| ------------- | ------------- |
+| Ivydata/whisper-small-japanese | **23.10%** |
+| Ivydata/wav2vec2-large-xlsr-53-japanese | **27.87%** |
+| jonatasgrosman/wav2vec2-large-xlsr-53-japanese | 34.18% |