File size: 7,256 Bytes
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Prepare Portuguese training data for Smart Turn fine-tuning.
Takes NURC-SP real conversation segments and creates labeled samples:
- "complete": 8s window ending at a turn boundary (speaker finished)
- "incomplete": 8s window from mid-turn (speaker still talking)
Output: FLAC files organized in the directory structure expected by
smart-turn's raw_to_hf_dataset.py
"""
from __future__ import annotations
import json
import logging
import uuid
from pathlib import Path
import numpy as np
import soundfile as sf
log = logging.getLogger(__name__)
TARGET_SR = 16000
WINDOW_SECONDS = 8
WINDOW_SAMPLES = WINDOW_SECONDS * TARGET_SR
OUTPUT_DIR = Path(__file__).parent / "data" / "smart_turn_pt_training" / "por"
def prepare_from_nurc(annotations_path: str, min_samples: int = 2000) -> dict:
"""Create training samples from NURC-SP annotations."""
with open(annotations_path) as f:
data = json.load(f)
stats = {"complete": 0, "incomplete": 0, "skipped": 0}
for conv_data in data:
audio, sr = sf.read(conv_data["audio_path"])
if audio.ndim > 1:
audio = audio.mean(axis=1)
audio = audio.astype(np.float32)
# Normalize
peak = np.max(np.abs(audio))
if peak > 0:
audio = audio / peak * 0.9
turns = conv_data["turns"]
conv_id = conv_data["conv_id"]
for i in range(len(turns)):
turn = turns[i]
turn_start = turn["start"]
turn_end = turn["end"]
turn_dur = turn_end - turn_start
# --- COMPLETE samples: window ending at turn boundary ---
if i > 0 and turn_dur > 0.5:
boundary_t = turn_start
end_sample = int(boundary_t * sr)
start_sample = max(0, end_sample - WINDOW_SAMPLES)
window = audio[start_sample:end_sample]
if len(window) >= sr: # At least 1s of audio
_save_sample(window, sr, "complete", "nofiller", conv_id, i)
stats["complete"] += 1
# --- INCOMPLETE samples: windows during the turn ---
if turn_dur >= 2.0:
# Sample at multiple points within the turn
n_points = max(1, int(turn_dur / 1.5)) # Every ~1.5s
for p in range(n_points):
# Position within the turn (avoid the very end)
frac = (p + 0.5) / (n_points + 1)
if frac > 0.85: # Don't sample too close to end
continue
mid_t = turn_start + turn_dur * frac
mid_sample = int(mid_t * sr)
start_sample = max(0, mid_sample - WINDOW_SAMPLES)
window = audio[start_sample:mid_sample]
if len(window) >= sr:
_save_sample(window, sr, "incomplete", "nofiller", conv_id, i, p)
stats["incomplete"] += 1
# Also create a complete sample at the END of the last turn
if i == len(turns) - 1 and turn_dur > 1.0:
end_sample = min(int(turn_end * sr), len(audio))
start_sample = max(0, end_sample - WINDOW_SAMPLES)
window = audio[start_sample:end_sample]
if len(window) >= sr:
_save_sample(window, sr, "complete", "nofiller", conv_id, i, 99)
stats["complete"] += 1
return stats
def _save_sample(
audio: np.ndarray,
sr: int,
endpoint: str, # "complete" or "incomplete"
filler: str, # "nofiller", "midfiller", "endfiller"
conv_id: str,
turn_idx: int,
sub_idx: int = 0,
) -> None:
"""Save a training sample as FLAC."""
# Pad/truncate to exactly 8 seconds
if len(audio) > WINDOW_SAMPLES:
audio = audio[-WINDOW_SAMPLES:]
elif len(audio) < WINDOW_SAMPLES:
padding = WINDOW_SAMPLES - len(audio)
audio = np.pad(audio, (padding, 0), mode="constant", constant_values=0)
# Add ~200ms silence at end (matching VAD behavior)
silence = int(0.2 * sr)
audio[-silence:] = 0.0
out_dir = OUTPUT_DIR / f"{endpoint}-{filler}"
out_dir.mkdir(parents=True, exist_ok=True)
filename = f"{conv_id}_t{turn_idx:03d}_s{sub_idx:02d}_{uuid.uuid4().hex[:8]}.flac"
sf.write(str(out_dir / filename), audio, sr, format="FLAC", subtype="PCM_16")
def prepare_from_tts(annotations_path: str) -> dict:
"""Create training samples from TTS dialogue annotations."""
with open(annotations_path) as f:
data = json.load(f)
stats = {"complete": 0, "incomplete": 0}
for conv_data in data:
audio, sr = sf.read(conv_data["audio_path"])
if audio.ndim > 1:
audio = audio.mean(axis=1)
audio = audio.astype(np.float32)
peak = np.max(np.abs(audio))
if peak > 0:
audio = audio / peak * 0.9
turns = conv_data["turns"]
conv_id = conv_data["conv_id"]
for i in range(len(turns)):
turn = turns[i]
turn_start = turn["start"]
turn_end = turn["end"]
turn_dur = turn_end - turn_start
# Complete at boundaries
if i > 0:
boundary_t = turn_start
end_sample = int(boundary_t * sr)
start_sample = max(0, end_sample - WINDOW_SAMPLES)
window = audio[start_sample:end_sample]
if len(window) >= sr:
_save_sample(window, sr, "complete", "nofiller", conv_id, i)
stats["complete"] += 1
# Incomplete mid-turn
if turn_dur >= 1.5:
n_points = max(1, int(turn_dur / 1.0))
for p in range(n_points):
frac = (p + 0.5) / (n_points + 1)
if frac > 0.8:
continue
mid_t = turn_start + turn_dur * frac
mid_sample = int(mid_t * sr)
start_sample = max(0, mid_sample - WINDOW_SAMPLES)
window = audio[start_sample:mid_sample]
if len(window) >= sr:
_save_sample(window, sr, "incomplete", "nofiller", conv_id, i, p)
stats["incomplete"] += 1
return stats
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
nurc_ann = "data/annotations/nurc_sp_annotations.json"
tts_ann = "data/annotations/portuguese_tts_annotations.json"
log.info("Preparing NURC-SP samples...")
s1 = prepare_from_nurc(nurc_ann)
log.info("NURC-SP: %s", s1)
log.info("Preparing TTS samples...")
s2 = prepare_from_tts(tts_ann)
log.info("TTS: %s", s2)
total_complete = s1["complete"] + s2["complete"]
total_incomplete = s1["incomplete"] + s2["incomplete"]
log.info("Total: %d complete + %d incomplete = %d samples",
total_complete, total_incomplete, total_complete + total_incomplete)
# List output
import os
for dirpath, dirnames, filenames in os.walk(OUTPUT_DIR):
if filenames:
log.info(" %s: %d files", os.path.basename(dirpath), len(filenames))
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