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
File size: 205 Bytes
e8c522a | 1 2 3 4 5 | from .configuration_speaker import SpeakerEncoderConfig
from .modeling_speaker import SpeakerEncoder, SpeakerEmbeddingOutput
__all__ = ["SpeakerEncoderConfig", "SpeakerEncoder", "SpeakerEmbeddingOutput"]
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