Feature Extraction
Transformers
Safetensors
vocbulwark_speaker_encoder
audio
speaker-recognition
speaker-embedding
speaker-verification
wav2vec2
vocbulwark
custom_code
Instructions to use mlr2000/vocoder-small-speaker-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlr2000/vocoder-small-speaker-encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mlr2000/vocoder-small-speaker-encoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mlr2000/vocoder-small-speaker-encoder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| """Config for the standalone VocBulwark speaker encoder.""" | |
| from transformers import PretrainedConfig | |
| class SpeakerEncoderConfig(PretrainedConfig): | |
| model_type = "vocbulwark_speaker_encoder" | |
| def __init__(self, speaker_embed_config=None, embedding_size=None, | |
| raw_sample_rate=22050, **kwargs): | |
| super().__init__(**kwargs) | |
| # The wav2vec2-based encoder's own config (dict), plus the produced | |
| # embedding dimension and the sample rate its input audio is expected at. | |
| self.speaker_embed_config = speaker_embed_config | |
| self.embedding_size = embedding_size | |
| self.raw_sample_rate = raw_sample_rate | |