def list_available_models(cls) -> Optional[PretrainedModelInfo]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ result = [] model = PretrainedModelInfo( pretrained_model_name="Joint_Intent_Slot_Assistant", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemonlpmodels/versions/1.0.0a5/files/Joint_Intent_Slot_Assistant.nemo", description="This models is trained on this https://github.com/xliuhw/NLU-Evaluation-Data dataset which includes 64 various intents and 55 slots. Final Intent accuracy is about 87%, Slot accuracy is about 89%.", ) result.append(model) return result
def list_available_models(cls) -> List[PretrainedModelInfo]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ result = [] model = PretrainedModelInfo( pretrained_model_name="speakerverification_speakernet", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/speakerverification_speakernet/versions/1.0.0rc1/files/speakerverification_speakernet.nemo", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:speakerverification_speakernet", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="ecapa_tdnn", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/ecapa_tdnn/versions/v1/files/ecapa_tdnn.nemo", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:ecapa_tdnn", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="titanet_large", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/titanet_large/versions/v0/files/titanet-l.nemo", description= "For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/titanet_large", ) result.append(model) return result
def list_available_models(cls) -> 'List[PretrainedModelInfo]': """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ list_of_models = [] model = PretrainedModelInfo( pretrained_model_name="UniGlow-22050Hz", location= "https://drive.google.com/file/d/18JO5heoz1pBicZnGGqJzAJYMpzxiDQDa/view?usp=sharing", description= "The model is trained on LJSpeech sampled at 22050Hz, and can be used as an universal vocoder", ) list_of_models.append(model) return list_of_models
def list_available_models(cls) -> 'List[PretrainedModelInfo]': """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ list_of_models = [] model = PretrainedModelInfo( pretrained_model_name="tts_en_fastpitch", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_en_fastpitch/versions/1.8.1/files/tts_en_fastpitch_align.nemo", description="This model is trained on LJSpeech sampled at 22050Hz with and can be used to generate female English voices with an American accent.", class_=cls, ) list_of_models.append(model) return list_of_models
def list_available_models(cls) -> 'Optional[Dict[str, str]]': """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ list_of_models = [] model = PretrainedModelInfo( pretrained_model_name="tts_melgan", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/tts_melgan/versions/1.0.0/files/tts_melgan.nemo", description= "This model is trained on LJSpeech sampled at 22050Hz, and has been tested on generating female English voices with an American accent.", class_=cls, ) list_of_models.append(model) return list_of_models
def list_available_models(cls) -> Optional[PretrainedModelInfo]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ result = [] model = PretrainedModelInfo( pretrained_model_name="ContextNet-192-WPE-1024-8x-Stride", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/ContextNet-192-WPE-1024-8x-Stride.nemo", description= "ContextNet initial implementation with CTC loss model trained on the Librispeech corpus and achieves a WER of 10.09% on test-other and 10.11% on dev-other.", ) result.append(model) return result
def list_available_models(cls) -> Optional[PretrainedModelInfo]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ result = [] result.append( PretrainedModelInfo( pretrained_model_name="sgdqa_bertbasecased", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/sgdqa_bertbasecased/versions/1.0.0/files/sgdqa_bertbasecased.nemo", description="Dialogue State Tracking model finetuned from NeMo BERT Base Cased on Google SGD dataset which has a joint goal accuracy of 59.72% on dev set and 45.85% on test set.", ) ) return result
def list_available_models(cls) -> 'List[PretrainedModelInfo]': """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ list_of_models = [] model = PretrainedModelInfo( pretrained_model_name="WaveGlow-22050Hz", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemottsmodels/versions/1.0.0a5/files/WaveGlow-22050Hz.nemo", description= "This model is trained on LJSpeech sampled at 22050Hz, and can be used as an universal vocoder.", class_=cls, ) list_of_models.append(model) return list_of_models
def list_available_models(cls) -> Optional[PretrainedModelInfo]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ result = [] model = PretrainedModelInfo( pretrained_model_name="NERModel", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemonlpmodels/versions/1.0.0a5/files/NERModel.nemo", description= "The model was trained on GMB (Groningen Meaning Bank) corpus for entity recognition and achieves 74.61 F1 Macro score.", ) result.append(model) return result
def list_available_models(cls) -> Optional[PretrainedModelInfo]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ results = [] model = PretrainedModelInfo( pretrained_model_name="stt_en_citrinet_256", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_256", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_256/versions/1.0.0rc1/files/stt_en_citrinet_256.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_en_citrinet_512", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_512", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_512/versions/1.0.0rc1/files/stt_en_citrinet_512.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_en_citrinet_1024", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_citrinet_1024", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_citrinet_1024/versions/1.0.0rc1/files/stt_en_citrinet_1024.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_en_conformer_ctc_small", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_small", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_small/versions/1.0.0rc1/files/stt_en_conformer_ctc_small.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_en_conformer_ctc_medium", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_medium", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_medium/versions/1.0.0rc1/files/stt_en_conformer_ctc_medium.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_en_conformer_ctc_large", description="For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_conformer_ctc_large", location="https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_conformer_ctc_large/versions/1.0.0rc1/files/stt_en_conformer_ctc_large.nemo", ) results.append(model) return results
def list_available_models(cls) -> Optional[PretrainedModelInfo]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ results = [] model = PretrainedModelInfo( pretrained_model_name="stt_zh_conformer_transducer_large", description= "For details about this model, please visit https://catalog.ngc.nvidia.com/orgs/nvidia/teams/nemo/models/stt_zh_conformer_transducer_large", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_conformer_transducer_large/versions/1.8.0/files/stt_zh_conformer_transducer_large.nemo", ) results.append(model) return results
def list_available_models(cls) -> Optional[List[PretrainedModelInfo]]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ result = [] model = PretrainedModelInfo( pretrained_model_name="MatchboxNet-3x1x64-v1", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/MatchboxNet-3x1x64-v1.nemo", description= "MatchboxNet model trained on Google Speech Commands dataset (v1, 30 classes) which obtains 97.32% accuracy on test set.", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="MatchboxNet-3x2x64-v1", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/MatchboxNet-3x2x64-v1.nemo", description= "MatchboxNet model trained on Google Speech Commands dataset (v1, 30 classes) which obtains 97.68% accuracy on test set.", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="MatchboxNet-3x1x64-v2", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/MatchboxNet-3x1x64-v2.nemo", description= "MatchboxNet model trained on Google Speech Commands dataset (v2, 35 classes) which obtains 97.12% accuracy on test set.", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="MatchboxNet-3x1x64-v2", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/MatchboxNet-3x1x64-v2.nemo", description= "MatchboxNet model trained on Google Speech Commands dataset (v2, 30 classes) which obtains 97.29% accuracy on test set.", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="MatchboxNet-3x1x64-v2-subset-task", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/MatchboxNet-3x1x64-v2-subset-task.nemo", description= "MatchboxNet model trained on Google Speech Commands dataset (v2, 10+2 classes) which obtains 98.2% accuracy on test set.", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="MatchboxNet-3x2x64-v2-subset-task", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/MatchboxNet-3x2x64-v2-subset-task.nemo", description= "MatchboxNet model trained on Google Speech Commands dataset (v2, 10+2 classes) which obtains 98.4% accuracy on test set.", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="MatchboxNet-VAD-3x2", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/MatchboxNet_VAD_3x2.nemo", description= "Voice Activity Detection MatchboxNet model trained on google speech command (v2) and freesound background data, which obtains 0.992 accuracy on testset from same source and 0.852 TPR for FPR=0.315 on testset (ALL) of AVA movie data", ) result.append(model) return result
def list_available_models(cls) -> Optional[List[PretrainedModelInfo]]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ results = [] model = PretrainedModelInfo( pretrained_model_name="vad_telephony_marblenet", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_telephony_marblenet", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_telephony_marblenet/versions/1.0.0rc1/files/vad_telephony_marblenet.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="vad_marblenet", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:vad_marblenet", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/vad_marblenet/versions/1.0.0rc1/files/vad_marblenet.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="commandrecognition_en_matchboxnet3x1x64_v1", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v1", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v1/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v1.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="commandrecognition_en_matchboxnet3x2x64_v1", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v1", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v1/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v1.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="commandrecognition_en_matchboxnet3x1x64_v2", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v2", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v2/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v2.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="commandrecognition_en_matchboxnet3x2x64_v2", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v2", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v2/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v2.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name= "commandrecognition_en_matchboxnet3x1x64_v2_subset_task", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x1x64_v2_subset_task", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x1x64_v2_subset_task/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x1x64_v2_subset_task.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name= "commandrecognition_en_matchboxnet3x2x64_v2_subset_task", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:commandrecognition_en_matchboxnet3x2x64_v2_subset_task", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/commandrecognition_en_matchboxnet3x2x64_v2_subset_task/versions/1.0.0rc1/files/commandrecognition_en_matchboxnet3x2x64_v2_subset_task.nemo", ) results.append(model) return results
def list_available_models(cls) -> Optional[PretrainedModelInfo]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ result = [] result.append( PretrainedModelInfo( pretrained_model_name="qa_squadv1.1_bertbase", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/qa_squadv1_1_bertbase/versions/1.0.0rc1/files/qa_squadv1.1_bertbase.nemo", description= "Question answering model finetuned from NeMo BERT Base Uncased on SQuAD v1.1 dataset which obtains an exact match (EM) score of 82.78% and an F1 score of 89.97%.", )) result.append( PretrainedModelInfo( pretrained_model_name="qa_squadv2.0_bertbase", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/qa_squadv2_0_bertbase/versions/1.0.0rc1/files/qa_squadv2.0_bertbase.nemo", description= "Question answering model finetuned from NeMo BERT Base Uncased on SQuAD v2.0 dataset which obtains an exact match (EM) score of 75.04% and an F1 score of 78.08%.", )) result.append( PretrainedModelInfo( pretrained_model_name="qa_squadv1_1_bertlarge", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/qa_squadv1_1_bertlarge/versions/1.0.0rc1/files/qa_squadv1.1_bertlarge.nemo", description= "Question answering model finetuned from NeMo BERT Large Uncased on SQuAD v1.1 dataset which obtains an exact match (EM) score of 85.44% and an F1 score of 92.06%.", )) result.append( PretrainedModelInfo( pretrained_model_name="qa_squadv2.0_bertlarge", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/qa_squadv2_0_bertlarge/versions/1.0.0rc1/files/qa_squadv2.0_bertlarge.nemo", description= "Question answering model finetuned from NeMo BERT Large Uncased on SQuAD v2.0 dataset which obtains an exact match (EM) score of 80.22% and an F1 score of 83.05%.", )) result.append( PretrainedModelInfo( pretrained_model_name="qa_squadv1_1_megatron_cased", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/qa_squadv1_1_megatron_cased/versions/1.0.0rc1/files/qa_squadv1.1_megatron_cased.nemo", description= "Question answering model finetuned from Megatron Cased on SQuAD v1.1 dataset which obtains an exact match (EM) score of 88.18% and an F1 score of 94.07%.", )) result.append( PretrainedModelInfo( pretrained_model_name="qa_squadv2.0_megatron_cased", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/qa_squadv2_0_megatron_cased/versions/1.0.0rc1/files/qa_squadv2.0_megatron_cased.nemo", description= "Question answering model finetuned from Megatron Cased on SQuAD v2.0 dataset which obtains an exact match (EM) score of 84.73% and an F1 score of 87.89%.", )) result.append( PretrainedModelInfo( pretrained_model_name="qa_squadv1.1_megatron_uncased", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/qa_squadv1_1_megatron_uncased/versions/1.0.0rc1/files/qa_squadv1.1_megatron_uncased.nemo", description= "Question answering model finetuned from Megatron Unased on SQuAD v1.1 dataset which obtains an exact match (EM) score of 87.61% and an F1 score of 94.00%.", )) result.append( PretrainedModelInfo( pretrained_model_name="qa_squadv2.0_megatron_uncased", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/qa_squadv2_0_megatron_uncased/versions/1.0.0rc1/files/qa_squadv2.0_megatron_uncased.nemo", description= "Question answering model finetuned from Megatron Uncased on SQuAD v2.0 dataset which obtains an exact match (EM) score of 84.48% and an F1 score of 87.65%.", )) return result
def list_available_models(cls) -> Optional[PretrainedModelInfo]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ results = [] model = PretrainedModelInfo( pretrained_model_name="QuartzNet15x5Base-En", description= "QuartzNet15x5 model trained on six datasets: LibriSpeech, Mozilla Common Voice (validated clips from en_1488h_2019-12-10), WSJ, Fisher, Switchboard, and NSC Singapore English. It was trained with Apex/Amp optimization level O1 for 600 epochs. The model achieves a WER of 3.79% on LibriSpeech dev-clean, and a WER of 10.05% on dev-other. Please visit https://ngc.nvidia.com/catalog/models/nvidia:nemospeechmodels for further details.", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemospeechmodels/versions/1.0.0a5/files/QuartzNet15x5Base-En.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_en_quartznet15x5", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_quartznet15x5", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_quartznet15x5/versions/1.0.0rc1/files/stt_en_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_zh_quartznet15x5", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_quartznet15x5", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_quartznet15x5/versions/1.0.0rc1/files/stt_zh_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_en_jasper10x5dr", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_en_jasper10x5dr", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_en_jasper10x5dr/versions/1.0.0rc1/files/stt_en_jasper10x5dr.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_ca_quartznet15x5", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ca_quartznet15x5", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ca_quartznet15x5/versions/1.0.0rc1/files/stt_ca_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_it_quartznet15x5", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_it_quartznet15x5", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_it_quartznet15x5/versions/1.0.0rc1/files/stt_it_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_fr_quartznet15x5", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_fr_quartznet15x5", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_fr_quartznet15x5/versions/1.0.0rc1/files/stt_fr_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_es_quartznet15x5", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_es_quartznet15x5", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_es_quartznet15x5/versions/1.0.0rc1/files/stt_es_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_de_quartznet15x5", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_de_quartznet15x5", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_de_quartznet15x5/versions/1.0.0rc1/files/stt_de_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_pl_quartznet15x5", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_pl_quartznet15x5", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_pl_quartznet15x5/versions/1.0.0rc1/files/stt_pl_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_ru_quartznet15x5", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_ru_quartznet15x5", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_ru_quartznet15x5/versions/1.0.0rc1/files/stt_ru_quartznet15x5.nemo", ) results.append(model) model = PretrainedModelInfo( pretrained_model_name="stt_zh_citrinet_512", description= "For details about this model, please visit https://ngc.nvidia.com/catalog/models/nvidia:nemo:stt_zh_citrinet_512", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/stt_zh_citrinet_512/versions/1.0.0rc1/files/stt_zh_citrinet_512.nemo", ) results.append(model) return results
def list_available_models(cls) -> Optional[Dict[str, str]]: """ This method returns a list of pre-trained model which can be instantiated directly from NVIDIA's NGC cloud. Returns: List of available pre-trained models. """ result = [] model = PretrainedModelInfo( pretrained_model_name="nmt_en_de_transformer12x2", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_de_transformer12x2/versions/1.0.0rc1/files/nmt_en_de_transformer12x2.nemo", description= "En->De translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_de_transformer12x2", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="nmt_de_en_transformer12x2", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_de_en_transformer12x2/versions/1.0.0rc1/files/nmt_de_en_transformer12x2.nemo", description= "De->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_de_en_transformer12x2", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="nmt_en_es_transformer12x2", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_es_transformer12x2/versions/1.0.0rc1/files/nmt_en_es_transformer12x2.nemo", description= "En->Es translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_es_transformer12x2", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="nmt_es_en_transformer12x2", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_es_en_transformer12x2/versions/1.0.0rc1/files/nmt_es_en_transformer12x2.nemo", description= "Es->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_es_en_transformer12x2", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="nmt_en_fr_transformer12x2", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_fr_transformer12x2/versions/1.0.0rc1/files/nmt_en_fr_transformer12x2.nemo", description= "En->Fr translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_fr_transformer12x2", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="nmt_fr_en_transformer12x2", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_fr_en_transformer12x2/versions/1.0.0rc1/files/nmt_fr_en_transformer12x2.nemo", description= "Fr->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_fr_en_transformer12x2", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="nmt_en_ru_transformer6x6", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_ru_transformer6x6/versions/1.0.0rc1/files/nmt_en_ru_transformer6x6.nemo", description= "En->Ru translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_ru_transformer6x6", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="nmt_ru_en_transformer6x6", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_ru_en_transformer6x6/versions/1.0.0rc1/files/nmt_ru_en_transformer6x6.nemo", description= "Ru->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_ru_en_transformer6x6", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="nmt_zh_en_transformer6x6", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_zh_en_transformer6x6/versions/1.0.0rc1/files/nmt_zh_en_transformer6x6.nemo", description= "Zh->En translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_zh_en_transformer6x6", ) result.append(model) model = PretrainedModelInfo( pretrained_model_name="nmt_en_zh_transformer6x6", location= "https://api.ngc.nvidia.com/v2/models/nvidia/nemo/nmt_en_zh_transformer6x6/versions/1.0.0rc1/files/nmt_en_zh_transformer6x6.nemo", description= "En->Zh translation model. See details here: https://ngc.nvidia.com/catalog/models/nvidia:nemo:nmt_en_zh_transformer6x6", ) result.append(model) return result