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: 661 Bytes
e8c522a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | """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
|