first commit

2026-08-15 14:43:56 +08:00
commit 63b8c28e1d
340 changed files with 59515 additions and 0 deletions
@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+import torch
+import torch.nn.functional as F
+
+DEFAULT_EDGE_SILENCE_MS = 250.0
+DEFAULT_EDGE_SILENCE_TOP_DB = 30.0
+
+
+def align_length(num_samples: int, multiple_of: int | None) -> int:
+    if multiple_of is None or multiple_of <= 0:
+        return int(num_samples)
+    if num_samples % multiple_of == 0:
+        return int(num_samples)
+    return int(((num_samples + multiple_of - 1) // multiple_of) * multiple_of)
+
+
+def pad_waveform_align_only(
+    waveform: torch.Tensor,
+    *,
+    multiple_of: int | None,
+) -> torch.Tensor:
+    if multiple_of is None or multiple_of <= 0:
+        return waveform
+
+    target_length = align_length(waveform.size(-1), multiple_of)
+    delta = target_length - waveform.size(-1)
+    if delta <= 0:
+        return waveform
+
+    return F.pad(waveform, (0, delta), "constant", 0.0)
+
+
+def normalize_edge_silence_duration(
+    waveform: torch.Tensor,
+    *,
+    sample_rate: int,
+    target_silence_duration_ms: float = DEFAULT_EDGE_SILENCE_MS,
+    top_db: float = DEFAULT_EDGE_SILENCE_TOP_DB,
+) -> torch.Tensor:
+    mono_waveform = waveform[0]
+    target_samples = int(round(float(sample_rate) * float(target_silence_duration_ms) / 1000.0))
+    amplitude = mono_waveform.abs()
+    peak = float(amplitude.max().item())
+    if peak <= 0.0:
+        waveform = waveform[..., :target_samples]
+        current_length = int(waveform.size(-1))
+        if current_length < target_samples:
+            waveform = F.pad(waveform, (0, target_samples - current_length), "constant", 0.0)
+        return waveform
+
+    threshold = peak * (10.0 ** (-float(top_db) / 20.0))
+    non_silent = torch.nonzero(amplitude > threshold, as_tuple=False).flatten()
+    first_non_silent = int(non_silent[0].item())
+    last_non_silent = int(non_silent[-1].item())
+
+    leading_silence_samples = first_non_silent
+    trailing_silence_samples = int(mono_waveform.numel()) - last_non_silent - 1
+
+    leading_delta = target_samples - leading_silence_samples
+    if leading_delta > 0:
+        waveform = F.pad(waveform, (leading_delta, 0), "constant", 0.0)
+    else:
+        trim_from_start = min(-leading_delta, int(waveform.size(-1)))
+        waveform = waveform[..., trim_from_start:]
+
+    trailing_delta = target_samples - trailing_silence_samples
+    if trailing_delta > 0:
+        return F.pad(waveform, (0, trailing_delta), "constant", 0.0)
+
+    trim_from_end = min(-trailing_delta, int(waveform.size(-1)))
+    if trim_from_end <= 0:
+        return waveform
+    return waveform[..., :-trim_from_end]
+
+
+def compute_num_audio_tokens(
+    num_samples: int, *, audio_samples_per_llm_token: int
+) -> int:
+    if num_samples % audio_samples_per_llm_token != 0:
+        raise ValueError(
+            f"Waveform length {num_samples} is not aligned to token hop {audio_samples_per_llm_token}."
+        )
+    return num_samples // audio_samples_per_llm_token