fix: accept in-memory audio arrays

README.md

@@ -236,6 +236,14 @@ The message flow follows the common Qwen multimodal pattern. The chat template i
 3. `processor(text=text, audio=audios)` computes Whisper input features and expands audio placeholders.
 4. `model.generate(...)` produces timestamped transcription and diarization text.
 
+`build_transcription_messages` also accepts a one-dimensional `numpy.ndarray` or `torch.Tensor` waveform, in addition to a local path. The waveform should be mono floating-point samples at the processor's configured sampling rate:
+
+```python
+messages = build_transcription_messages(waveform, prompt="Transcribe and diarize this audio.")
+```
+
+The helper preserves the array in the message and converts tensors to NumPy before handing them to the Transformers audio loader.
+
 The repository's Hugging Face loader uses an explicit attention policy instead of
 accepting a silent Transformers fallback: `flash_attention_4`, `flash_attention_3`,
 `flash_attention_2`, `sdpa`, then `eager`. Unavailable candidates and the selected

README_zh.md

@@ -236,6 +236,14 @@ for segment in parse_transcript(result["text"]):
 3. `processor(text=text, audio=audios)` 计算 Whisper 输入特征并展开音频占位符。
 4. `model.generate(...)` 生成带时间戳的转写与说话人分离文本。
 
+除本地文件路径外，`build_transcription_messages` 也接受一维 `numpy.ndarray` 或 `torch.Tensor` 波形。波形应为单声道浮点采样，并使用 processor 配置的采样率：
+
+```python
+messages = build_transcription_messages(waveform, prompt="Transcribe and diarize this audio.")
+```
+
+该辅助函数会保留消息中的数组，并在交给 Transformers 音频 loader 前将 Tensor 转换为 NumPy。
+
 仓库的 Hugging Face 加载器使用显式的 attention 策略，不接受 Transformers 的静默回退：候选顺序为
 `flash_attention_4`、`flash_attention_3`、`flash_attention_2`、`sdpa`，最后才是 `eager`。
 不可用的候选和最终选择都会写入日志；如果最终落到 `eager`，会额外发出 warning，因为它会为长音频物化二次方大小的 attention 张量。

moss_transcribe_diarize/inference_utils.py

@@ -112,8 +112,10 @@ def load_audio_av(audio: str, sampling_rate: int) -> np.ndarray:
     return (np.concatenate(chunks).astype(np.float32) / 32768.0).astype(np.float32, copy=False)
 
 
-def load_audio_item(audio: str | np.ndarray, sampling_rate: int) -> np.ndarray:
+def load_audio_item(audio: str | np.ndarray | torch.Tensor, sampling_rate: int) -> np.ndarray:
     """Load audio with Transformers' loader, using PyAV for media containers."""
+    if torch.is_tensor(audio):
+        audio = audio.detach().cpu().numpy()
     if isinstance(audio, str) and _is_likely_video_path(audio):
         return load_audio_av(audio, sampling_rate=sampling_rate)
     try:
@@ -139,19 +141,31 @@ def process_audio_info(messages: list[dict[str, Any]], sampling_rate: int):
         for item in content:
             if item.get("type") != "audio":
                 continue
-            audio = item.get("audio") or item.get("audio_url") or item.get("url") or item.get("path")
+            # Do not use ``or`` here: NumPy arrays and tensors have no single
+            # truth value when they contain more than one sample.
+            audio = None
+            for key in ("audio", "audio_url", "url", "path"):
+                value = item.get(key)
+                if value is not None:
+                    audio = value
+                    break
             if audio is None:
                 raise ValueError("Audio content must include audio, audio_url, url, or path.")
             audios.append(load_audio_item(audio, sampling_rate=sampling_rate))
     return audios
 
 
-def build_transcription_messages(audio_path: str | Path, prompt: str = DEFAULT_PROMPT) -> list[dict[str, Any]]:
+def build_transcription_messages(
+    audio_path: str | Path | np.ndarray | torch.Tensor,
+    prompt: str = DEFAULT_PROMPT,
+) -> list[dict[str, Any]]:
+    """Build a chat message for a path or an in-memory mono waveform."""
+    audio = str(audio_path) if isinstance(audio_path, Path) else audio_path
     return [
         {
             "role": "user",
             "content": [
-                {"type": "audio", "audio": str(audio_path)},
+                {"type": "audio", "audio": audio},
                 {"type": "text", "text": prompt.strip() or DEFAULT_PROMPT},
             ],
         }

tests/test_inference_utils.py

@@ -0,0 +1,58 @@
+import unittest
+from unittest.mock import patch
+
+import numpy as np
+import torch
+
+from moss_transcribe_diarize.inference_utils import (
+    DEFAULT_PROMPT,
+    build_transcription_messages,
+    load_audio_item,
+    process_audio_info,
+)
+
+
+class InferenceUtilsTest(unittest.TestCase):
+    @patch("moss_transcribe_diarize.inference_utils.load_audio_item")
+    def test_process_audio_info_accepts_numpy_waveform(self, load_audio_item):
+        waveform = np.array([0.1, -0.2, 0.3], dtype=np.float32)
+        load_audio_item.return_value = waveform
+        messages = [{"role": "user", "content": [{"type": "audio", "audio": waveform}]}]
+
+        audios = process_audio_info(messages, sampling_rate=16_000)
+
+        self.assertEqual(audios, [waveform])
+        load_audio_item.assert_called_once_with(waveform, sampling_rate=16_000)
+
+    @patch("moss_transcribe_diarize.inference_utils.load_audio_item")
+    def test_process_audio_info_accepts_tensor_without_truth_value_error(self, load_audio_item):
+        waveform = torch.tensor([0.1, -0.2, 0.3])
+        load_audio_item.return_value = waveform.numpy()
+        messages = [{"role": "user", "content": [{"type": "audio", "audio": waveform}]}]
+
+        process_audio_info(messages, sampling_rate=16_000)
+
+        load_audio_item.assert_called_once_with(waveform, sampling_rate=16_000)
+
+    @patch("moss_transcribe_diarize.inference_utils.load_audio")
+    def test_load_audio_item_converts_tensor_to_numpy(self, load_audio):
+        waveform = torch.tensor([0.1, -0.2, 0.3])
+        load_audio.return_value = waveform.numpy()
+
+        load_audio_item(waveform, sampling_rate=16_000)
+
+        loaded_audio = load_audio.call_args.args[0]
+        self.assertIsInstance(loaded_audio, np.ndarray)
+        np.testing.assert_array_equal(loaded_audio, waveform.numpy())
+
+    def test_build_transcription_messages_preserves_in_memory_audio(self):
+        waveform = np.zeros(8, dtype=np.float32)
+
+        messages = build_transcription_messages(waveform, prompt=" ")
+
+        self.assertIs(messages[0]["content"][0]["audio"], waveform)
+        self.assertEqual(messages[0]["content"][1]["text"], DEFAULT_PROMPT)
+
+
+if __name__ == "__main__":
+    unittest.main()