Spaces:
Sleeping
Sleeping
File size: 2,311 Bytes
9f76952 | 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 71 72 73 |
import os
import torch
import torchaudio
import torch.serialization
from pyannote.core import Segment, Timeline
# --- 1. MONKEY PATCH (Fixes PyTorch 2.6 Security Error) ---
original_load = torch.serialization.load
def forced_load(f, map_location=None, pickle_module=None, **kwargs):
kwargs['weights_only'] = False
return original_load(f, map_location=map_location, pickle_module=pickle_module, **kwargs)
torch.load = forced_load
torch.serialization.load = forced_load
# ---------------------------------------------------------
#Model : neural network, Segmentation : training logic, get_protocol : dataset loader, pl.Trainer : training engine
from pyannote.audio import Model
from pyannote.audio.tasks import Segmentation
from pyannote.database import get_protocol, FileFinder
import pytorch_lightning as pl
os.environ["PYANNOTE_DATABASE_CONFIG"] = "database.yml"
def train_segmentation():
# 2. PREPROCESSORS
def get_annotated(file):
info = torchaudio.info(file["audio"])
# Calculate duration: total frames / sample rate
duration = info.num_frames / info.sample_rate
# Return the 'Timeline' object the library is looking for
return Timeline([Segment(0, duration)])
preprocessors = {
"audio": FileFinder(),
"annotated": get_annotated,
}
# 3. LOAD PROTOCOL
print("Loading Hindi-Bhojpuri Protocol...")
protocol = get_protocol(
'HindiBhojpuri.SpeakerDiarization.Segmentation',
preprocessors=preprocessors
)
# 4. SETUP TASK
seg_task = Segmentation(
protocol,
duration=2.0,
batch_size=4,
num_workers=0
)
# 5. LOAD MODEL - Start from an English-trained segmentation model, and adapt it to Hindi/Bhojpuri.” This is transfer learning, not training from scratch.
print("Attempting to load model...")
model = Model.from_pretrained("pyannote/segmentation-3.0")
model.task = seg_task
# 6. TRAINER
trainer = pl.Trainer(
accelerator="cpu",
max_epochs=5,
default_root_dir="training_results"
)
# 7. START
print("--- Starting Fine-tuning ---")
trainer.fit(model)
if __name__ == "__main__":
train_segmentation() |