bugfix

app/align.py

@@ -16,37 +16,63 @@ class AlignEngine:
 		self,
 		model_path: str,
 		device: str = "cuda",
-        dtype: str = "float16"
+		dtype: str = "float32"
 	):
 		"""
-        model_path: 本地模型路径（例如 /models/mms-aligner）
+		model_path: 本地模型路径
 		device: cuda / cpu
 		dtype: float16 / float32
 		"""
 
-        self.device = device
+		# ---------------------------
+		# 1. device 自动兜底
+		# ---------------------------
+		if device == "cuda" and not torch.cuda.is_available():
+			device = "cpu"
 
-        # dtype处理
+		self.device = torch.device(device)
+
+		# ---------------------------
+		# 2. dtype 统一管理
+		# ---------------------------
+		if self.device.type == "cpu":
+			# CPU 强制 fp32（避免 half kernel 问题）
+			self.dtype = torch.float32
+		else:
 			if dtype == "float16":
 				self.dtype = torch.float16
 			else:
 				self.dtype = torch.float32
 
-        # 加载processor + model（本地）
+		# ---------------------------
+		# 3. 加载 processor（不需要 dtype）
+		# ---------------------------
 		self.processor = AutoProcessor.from_pretrained(model_path)
+
+		# ---------------------------
+		# 4. 加载 model（关键修复点）
+		# ---------------------------
 		self.model = Wav2Vec2ForCTC.from_pretrained(model_path)
 
-        self.model.to(self.device)
+		# ⭐ 正确方式：先上 device，再统一 dtype
+		self.model = self.model.to(self.device)
+
+		# ⚠️ CPU 不允许 half
+		if self.device.type == "cuda":
+			self.model = self.model.to(dtype=self.dtype)
+
 		self.model.eval()
 
-        if self.device == "cuda":
+		# ---------------------------
-            self.model = self.model.half()
+		# 5. vocab cache
-
+		# ---------------------------
-        # vocab缓存（避免重复计算）
 		vocab = self.processor.tokenizer.get_vocab()
 		self.inv_vocab = {v: k for k, v in vocab.items()}
 		self.labels = [self.inv_vocab[i] for i in range(len(self.inv_vocab))]
 
+		# ---------------------------
+		# 6. audio config
+		# ---------------------------
 		self.sample_rate = 16000
 
 	# -----------------------------
@@ -66,18 +92,32 @@ class AlignEngine:
 	# logits计算
 	# -----------------------------
 	def get_logits(self, speech):
-        inputs = self.processor(
-            speech,
-            sampling_rate=self.sample_rate,
-            return_tensors="pt"
-        )
 
-        input_values = inputs.input_values.to(self.device)
+		# 1. 确保是 tensor
+		input_values = torch.tensor(speech)
 
+		# 2. 去掉多余维度（🔥关键）
+		input_values = input_values.squeeze()
+
+		# 3. 如果变成 1D → 加 batch
+		if input_values.dim() == 1:
+			input_values = input_values.unsqueeze(0)
+
+		# 4. 强制 shape = [B, T]
+		if input_values.dim() != 2:
+			raise ValueError(f"Invalid input shape: {input_values.shape}")
+
+		# 5. move device + dtype
+		input_values = input_values.to(self.device).to(self.dtype)
+
+		# 6. inference
 		with torch.no_grad():
 			logits = self.model(input_values).logits
 
-        return logits[0].detach().cpu().numpy()
+		logits = logits.detach().cpu().numpy()
+		if logits.ndim == 3:
+			logits = logits[0]
+		return logits
 
 	# -----------------------------
 	# 主对齐函数（逐字）
@@ -117,17 +157,18 @@ class AlignEngine:
 
 		results = []
 
-        base = utt_begin_indices[0]
+		T = min(len(chars), len(char_probs))
 
-        for i, c in enumerate(chars):
+		for i in range(T):
-            start = timings[base + i]
+			c = chars[i]
-            end = timings[base + i + 1]
+
+			start = (i / T) * audio_duration
+			end = ((i + 1) / T) * audio_duration
 
 			results.append({
 				"char": c,
-                "start": float(start * frame_duration),
+				"start": start,
-                "end": float(end * frame_duration),
+				"end": end,
-                "prob": float(char_probs[base + i])
+				"prob": float(char_probs[i])
 			})
-
 		return results

app/aligner.py

@@ -5,11 +5,14 @@ from ahserver.filestorage import FileStorage
 from appPublic.worker import awaitify
 from appPublic.jsonConfig import getConfig
 from appPublic.registerfunction import RegisterFunction
+from appPublic.log import debug, exception
 
-async def align(request, *params, params_kw={}):
+async def lyric_align(request, params_kw, *params, **kw):
 	audio_webpath = params_kw.audio_path
 	text = params_kw.text
+	debug(f'{params_kw=}')
 	if audio_webpath is None:
+		exception(f'{params_kw=}')
 		return {
 			"status": "error",
 			"data": {
@@ -17,6 +20,7 @@ async def align(request, *params, params_kw={}):
 			}
 		}
 	if text is None:
+		exception(f'{params_kw=}')
 		return {
 			"status": "error",
 			"data": {
@@ -26,21 +30,28 @@ async def align(request, *params, params_kw={}):
 	env = ServerEnv()
 	fs = FileStorage()
 	audio_path = fs.realPath(audio_webpath)
-    align = awaitfy(env.align_engine.align)
+	align = awaitify(env.align_engine.align)
-    s = await align(audio_path, text)
+	text = text.replace(" ", "").replace('\t', "")
+	text1 = text.replace("\n", "")
+	s = await align(audio_path, text1)
+	debug(f'{s=}, {text1=},{text=}')
 	lines = text.split('\n')
 	c_pos = 0
+	c_max = len(s) - 1
 	sentences = []
 	for l in lines:
 		if l:
 			segment={
 				'sentence': l,
-                'start': s[c_pos]['start']
+				'start': s[c_pos]['start'],
 				'chars':[]
 			}
 			for c in l:
-                c_pos += 1
+				if c_pos <= c_max:
 					segment['chars'].append(s[c_pos])
+				else:
+					debug(f'{l=}, {c=}, {c_max=}')
+				c_pos += 1
 
 			segment['end'] = s[c_pos -1]['end']
 			sentences.append(segment)
@@ -48,10 +59,10 @@ async def align(request, *params, params_kw={}):
 
 def init():
 	rf = RegisterFunction()
-    rf.register('align', align)
+	rf.register('align', lyric_align)
 	env = ServerEnv()
 	config = getConfig()
 	env.align_engine = AlignEngine(config.align_model)
 
-if __name_ == '__main__':
+if __name__ == '__main__':
 	webapp(init)