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trzy
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Mar 1, 2024
| class VoiceSample(CreatedAtMixin, table=True): | ||
| id: Optional[int] = Field(default=None, primary_key=True) | ||
| filepath: str = Field(...) | ||
| speaker_embeddings: dict = Field(default={}, sa_column=Column(JSON)) |
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If the key is embedding model can we name this explicitly speaker_embeddings_by_model?
Bakuutin
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Apr 6, 2024
| # Use batch operations to support SQLite ALTER TABLE for adding constraints | ||
| with op.batch_alter_table('utterance', schema=None) as batch_op: | ||
| batch_op.add_column(sa.Column('person_id', sa.Integer(), nullable=True)) | ||
| batch_op.create_foreign_key('fk_utterance_person', 'person', ['person_id'], ['id']) |
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Would it make sense to store the vector embedding of the voice here? This way, we would be able to
- Show distinct speakers in the UI even without having the Persons in the DB
- On creating a new person, easily find all the instances in the past when that person spoke by fetching all the utterances with similar enough voice embeddings
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Adds person and voice sample models. Also adds an abstract identification service which we can implement for different embedding models/strategies.
Persons can be enrolled from the CLI, but we will be able to tag them from the UI as well. actual identification not implemented yet.