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import nemo.collections.asr as nemo_asr | |
import torch | |
def stt_es_model(): | |
""" | |
Load and return the pre-trained Spanish ASR model. | |
This function loads the pre-trained EncDecCTCModelBPE model from NVIDIA's NeMo collection. | |
The model is configured to use a GPU if available, otherwise it defaults to CPU. | |
Returns: | |
nemo_asr.models.EncDecCTCModelBPE: The loaded ASR model. | |
Example usage: | |
asr_model = stt_es_model() | |
""" | |
# Load the pre-trained model | |
asr_model = nemo_asr.models.EncDecCTCModelBPE.from_pretrained("nvidia/stt_es_fastconformer_hybrid_large_pc") | |
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | |
asr_model = asr_model.to(device) | |
return asr_model | |
def stt_es_process(asr_model, audio_file): | |
""" | |
Transcribe an audio file using the given ASR model. | |
Args: | |
asr_model (nemo_asr.models.EncDecCTCModelBPE): The ASR model to use for transcription. | |
Example: asr_model = stt_es_model() | |
audio_file (str): Path to the audio file to be transcribed. | |
Example: "path/to/audio_file.wav" | |
Returns: | |
list: A list containing the transcribed text. | |
Example: ["transcribed text"] | |
""" | |
text = asr_model.transcribe(paths2audio_files=[audio_file], batch_size=1) | |
return text | |