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To use this model, you need to install the NeMo toolkit:
pip install nemo-toolkit==2.0.0rc0 nemo-toolkit[asr]==2.0.0rc0
Here's a basic example of how to use the model:
import nemo.collections.asr as nemo_asr
# Load the model
nemo_model = nemo_asr.models.ASRModel.restore_from("/path/to/parakeet-ja.nemo")
# Transcribe audio files
audio_files = ["path/to/audio1.wav", "path/to/audio2.wav"]
transcriptions = nemo_model.transcribe(audio_files)
# Print transcriptions
for audio_file, transcription in zip(audio_files, transcriptions):
print(f"Transcription for {audio_file}: {transcription}")
The following table compares the performance of the NeMo model (Parakeet-JA) with Whisper v2 large and Whisper v3 large across different Japanese ASR datasets:
| Model | Dataset | WER | CER |
|---|---|---|---|
| Whisper v2 large | japanese-asr/ja_asr.reazonspeech_test | 1.1378 | 0.3472 |
| japanese-asr/ja_asr.jsut_basic5000 | 0.8988 | 0.1063 | |
| japanese-asr/ja_asr.common_voice_8_0 | 1.0314 | 0.1594 | |
| Whisper v3 large | japanese-asr/ja_asr.reazonspeech_test | 0.9685 | 0.2107 |
| japanese-asr/ja_asr.jsut_basic5000 | 0.9936 | 0.1360 | |
| japanese-asr/ja_asr.common_voice_8_0 | 1.0178 | 0.1548 | |
| NeMo (parakeet-ctc-1.1b-ja) | japanese-asr/ja_asr.reazonspeech_test | 0.7785 | 0.1521 |
| japanese-asr/ja_asr.jsut_basic5000 | 0.9462 | 0.1291 | |
| japanese-asr/ja_asr.common_voice_8_0 | 1.0002 | 0.1290 |
For more detailed information on using ASR models with the NeMo toolkit, please refer to the NeMo ASR documentation.