Initial commit: FunASR Speech Recognition Toolkit

Add complete FunASR codebase including models, runtime, and documentation.
2026-07-09 22:38:58 +08:00
commit 6116b1f3c6
3683 changed files with 990984 additions and 0 deletions
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+from itertools import groupby
+
+import soundfile as sf
+import torch
+import torchaudio
+import torchaudio.functional as F
+
+
+def load_audio(wav_path, rate: int = None, offset: float = 0, duration: float = None):
+    """Load audio.
+    
+        Args:
+            wav_path: TODO.
+            rate: TODO.
+            offset: TODO.
+            duration: TODO.
+        """
+    with sf.SoundFile(wav_path) as f:
+        start_frame = int(offset * f.samplerate)
+        if duration is None:
+            frames_to_read = f.frames - start_frame
+        else:
+            frames_to_read = int(duration * f.samplerate)
+        f.seek(start_frame)
+        audio_data = f.read(frames_to_read, dtype="float32")
+    audio_tensor = torch.from_numpy(audio_data)
+    if rate is not None and f.samplerate != rate:
+        if audio_tensor.ndim == 1:
+            audio_tensor = audio_tensor.unsqueeze(0)
+        else:
+            audio_tensor = audio_tensor.T
+        resampler = torchaudio.transforms.Resample(orig_freq=f.samplerate, new_freq=rate)
+        audio_tensor = resampler(audio_tensor)
+        if audio_tensor.shape[0] == 1:
+            audio_tensor = audio_tensor.squeeze(0)
+    return audio_tensor, rate if rate is not None else f.samplerate
+
+
+def forced_align(log_probs: torch.Tensor, targets: torch.Tensor, blank: int = 0):
+    """Forced align.
+    
+        Args:
+            log_probs: TODO.
+            targets: TODO.
+            blank: TODO.
+        """
+    items = []
+    try:
+        # The current version only supports batch_size==1.
+        log_probs, targets = log_probs.unsqueeze(0).cpu(), targets.unsqueeze(0).cpu()
+        assert log_probs.shape[1] >= targets.shape[1]
+        alignments, scores = F.forced_align(log_probs, targets, blank=blank)
+        alignments, scores = alignments[0], torch.exp(scores[0]).tolist()
+        # use enumerate to keep track of the original indices, then group by token value
+        for token, group in groupby(enumerate(alignments), key=lambda item: item[1]):
+            if token == blank:
+                continue
+            group = list(group)
+            start = group[0][0]
+            end = start + len(group)
+            score = max(scores[start:end])
+            items.append(
+                {
+                    "token": token.item(),
+                    "start_time": start,
+                    "end_time": end,
+                    "score": round(score, 3),
+                }
+            )
+    except:
+        pass
+    return items