| | |
| | | from funasr.models.joint_net.joint_network import JointNetwork |
| | | from funasr.models.e2e_asr import ESPnetASRModel |
| | | from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerOnline, ParaformerBert, BiCifParaformer, ContextualParaformer |
| | | from funasr.models.e2e_asr_contextual_paraformer import NeatContextualParaformer |
| | | from funasr.models.e2e_tp import TimestampPredictor |
| | | from funasr.models.e2e_asr_mfcca import MFCCA |
| | | from funasr.models.e2e_uni_asr import UniASR |
| | |
| | | paraformer_bert=ParaformerBert, |
| | | bicif_paraformer=BiCifParaformer, |
| | | contextual_paraformer=ContextualParaformer, |
| | | neatcontextual_paraformer=NeatContextualParaformer, |
| | | mfcca=MFCCA, |
| | | timestamp_prediction=TimestampPredictor, |
| | | ), |
| | |
| | | normalize = None |
| | | |
| | | # 4. Encoder |
| | | |
| | | if getattr(args, "encoder", None) is not None: |
| | | encoder_class = encoder_choices.get_class(args.encoder) |
| | | encoder = encoder_class(input_size, **args.encoder_conf) |
| | |
| | | |
| | | # 7. Build model |
| | | |
| | | if encoder.unified_model_training: |
| | | if hasattr(encoder, 'unified_model_training') and encoder.unified_model_training: |
| | | model = UnifiedTransducerModel( |
| | | vocab_size=vocab_size, |
| | | token_list=token_list, |