Rename to piper

src/python/piper_train/norm_audio/trim.py

@@ -0,0 +1,54 @@
+from typing import Optional, Tuple
+
+import numpy as np
+
+from .vad import SileroVoiceActivityDetector
+
+
+def trim_silence(
+    audio_array: np.ndarray,
+    detector: SileroVoiceActivityDetector,
+    threshold: float = 0.2,
+    samples_per_chunk=480,
+    sample_rate=16000,
+    keep_chunks_before: int = 2,
+    keep_chunks_after: int = 2,
+) -> Tuple[float, Optional[float]]:
+    """Returns the offset/duration of trimmed audio in seconds"""
+    offset_sec: float = 0.0
+    duration_sec: Optional[float] = None
+    first_chunk: Optional[int] = None
+    last_chunk: Optional[int] = None
+    seconds_per_chunk: float = samples_per_chunk / sample_rate
+
+    chunk = audio_array[:samples_per_chunk]
+    audio_array = audio_array[samples_per_chunk:]
+    chunk_idx: int = 0
+
+    # Determine main block of speech
+    while len(audio_array) > 0:
+        prob = detector(chunk, sample_rate=sample_rate)
+        is_speech = prob >= threshold
+
+        if is_speech:
+            if first_chunk is None:
+                # First speech
+                first_chunk = chunk_idx
+            else:
+                # Last speech so far
+                last_chunk = chunk_idx
+
+        chunk = audio_array[:samples_per_chunk]
+        audio_array = audio_array[samples_per_chunk:]
+        chunk_idx += 1
+
+    if (first_chunk is not None) and (last_chunk is not None):
+        first_chunk = max(0, first_chunk - keep_chunks_before)
+        last_chunk = min(chunk_idx, last_chunk + keep_chunks_after)
+
+        # Compute offset/duration
+        offset_sec = first_chunk * seconds_per_chunk
+        last_sec = (last_chunk + 1) * seconds_per_chunk
+        duration_sec = last_sec - offset_sec
+
+    return offset_sec, duration_sec