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fda93d9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 | """Speaker classifier scaffold for multi-task training and evaluation.
This module provides a small PyTorch `SpeakerClassifier` that maps embeddings
(or pooled encoder outputs) to speaker logits, plus helpers to build speaker
mappings from manifests.
"""
import json
from pathlib import Path
from typing import Dict, List
try:
import torch
import torch.nn as nn
except Exception:
torch = None
nn = None
class SpeakerClassifier:
"""A light-weight wrapper that exposes an API-compatible classifier.
If PyTorch is available, `SpeakerClassifier.model` is a `nn.Module`.
Otherwise this is a placeholder to keep the dependency optional in tests.
"""
def __init__(self, input_dim: int, num_speakers: int, dropout: float = 0.1):
self.input_dim = input_dim
self.num_speakers = num_speakers
self.dropout = dropout
if torch is not None and nn is not None:
self.model = nn.Sequential(
nn.Dropout(p=dropout),
nn.Linear(input_dim, num_speakers),
)
else:
self.model = None
def forward(self, x):
if self.model is None:
raise RuntimeError("PyTorch not available for SpeakerClassifier")
return self.model(x)
def build_speaker_map(manifest_paths: List[str]) -> Dict[str, int]:
"""Read JSONL manifest(s) and return a speaker->id mapping.
The manifest format: each line is JSON with optional "speaker" key.
Labels are returned in deterministic sorted order.
"""
speakers = set()
for p in manifest_paths:
pth = Path(p)
if not pth.exists():
continue
with open(pth, "r", encoding="utf-8") as fh:
for line in fh:
line = line.strip()
if not line:
continue
try:
obj = json.loads(line)
except Exception:
continue
spk = obj.get("speaker")
if spk is not None:
speakers.add(str(spk))
sorted_spks = sorted(speakers)
return {s: i for i, s in enumerate(sorted_spks)}
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