update

pyproject.toml

@@ -1,5 +1,5 @@
 [project]
-name = "study-asr"
+name = "audio-model"
 version = "0.1.0"
 description = "Add your description here"
 readme = "README.md"

src/dataset.py

@@ -9,7 +9,7 @@ from torchaudio.transforms import Resample, TimeStretch
 from pathlib import Path
 import pandas as pd
 from typing import List, TypedDict
-from handle.text_normalizer import collapse_spaces, normalize_extended_uyghur_characters
+from handle.text_normalizer import clean_input_text
 from tokenizer import ASRTokenizer
 
 
@@ -55,8 +55,8 @@ class NoiseAugmentor:
             noise_waveform = Resample(sr, self.sample_rate)(noise_waveform)
         
         # Convert to mono if it is setro.
-        if waveform.shape[0] > 1:
-            waveform = waveform.mean(dim=0, keepdim=True)
+        if noise_waveform.shape[0] > 1:
+            noise_waveform = noise_waveform.mean(dim=0, keepdim=True)
         
         # 3. 截取或填充，使其长度与语音一致
         sig_len = waveform.shape[1]
@@ -69,7 +69,7 @@ class NoiseAugmentor:
         else:
             full_noise = torch.zeros_like(waveform)
             start = random.randint(0, sig_len - noise_len)
-            full_noise[:, start : start + noise_len] = noise_waveform
+            full_noise[:, start:start + noise_len] = noise_waveform
             noise_waveform = full_noise
             
         # 4. 设定随机信噪比 SNR (5dB 到 20dB)
@@ -124,8 +124,8 @@ class CommonVoiceDataset(Dataset[BatchItem]):
         self.gain_down = Gain(min_gain_in_db=-15, max_gain_in_db=-8, p=1.0, output_type='tensor')
         self.pitch_up = PitchShift(min_transpose_semitones=1, max_transpose_semitones=4, p=1.0, sample_rate=self.sample_rate, output_type='tensor')
         self.pitch_down = PitchShift(min_transpose_semitones=-4, max_transpose_semitones=-1, p=1.0, sample_rate=self.sample_rate, output_type='tensor')
-        self.lowpass = LowPassFilter(min_cutoff_freq=100, max_cutoff_freq=2000, p=1.0, output_type='tensor')
-        self.highpass = HighPassFilter(min_cutoff_freq=1000, max_cutoff_freq=2000, p=1.0, output_type='tensor')
+        self.lowpass = LowPassFilter(min_cutoff_freq=1000, max_cutoff_freq=3000, p=1.0, sample_rate=self.sample_rate, output_type='tensor')
+        self.highpass = HighPassFilter(min_cutoff_freq=1000, max_cutoff_freq=3000, p=1.0, sample_rate=self.sample_rate, output_type='tensor')
         self.apply_ir = ApplyImpulseResponse(ir_paths=corridor_noise_dir, convolve_mode='same', p=1, output_type="tensor")
         self.polarity_inversion = PolarityInversion(p=1.0, output_type="tensor")
 
@@ -144,7 +144,7 @@ class CommonVoiceDataset(Dataset[BatchItem]):
             waveform = waveform.mean(dim=0, keepdim=True)
         
         # Normalization
-        waveform = waveform / waveform.abs().max()
+        waveform = waveform / max(waveform.abs().max(), 1e-8)
         
         # Clip waveform exceeds from max length.
         if waveform.shape[1] > self.max_audio_len:
@@ -159,7 +159,7 @@ class CommonVoiceDataset(Dataset[BatchItem]):
         if random.random() < 0.6:
             waveform = self._stretch_or_compress(waveform=waveform)
         
-        if random.random() < 0.7:
+        if random.random() < 0.5:
             waveform = self.noise_augmentor.apply_real_noise(waveform)
         
         if random.random() < 0.3:
@@ -167,7 +167,7 @@ class CommonVoiceDataset(Dataset[BatchItem]):
 
         # torch_audiomentations: [1, time] -> [1, 1, time]
         if waveform.dim() == 2:
-            waveform_3d = waveform.unsqueeze(0)
+            waveform_3d: Tensor = waveform.unsqueeze(0)
         # 随机选择一种物理特性增强 (互斥区)
         choice = random.random()
         if choice < 0.25: # [0.00 - 0.25] 25% 概率：增益
@@ -191,7 +191,6 @@ class CommonVoiceDataset(Dataset[BatchItem]):
             waveform_3d = 0.8 * dry + 0.2 * wet            
         else: # [0.95 - 1.00] 5% 概率：极性翻转
             waveform_3d = self.polarity_inversion(waveform_3d, sample_rate=self.sample_rate)
-
         
         #  [1, 1, time] -> [1, time]
         waveform = waveform_3d.squeeze(0)
@@ -249,7 +248,7 @@ class CommonVoiceDataset(Dataset[BatchItem]):
     def __getitem__(self, index) -> BatchItem:
         row: TsvFormat = self.data.iloc[index]
         audio_path: Path = self.audio_dir / row['path']
-        text: str = normalize_extended_uyghur_characters(collapse_spaces(row['sentence'].strip()))
+        text: str = clean_input_text(row['sentence'].strip())
 
         waveform = self._load_audio(audio_path=audio_path)
         waveform = self._augment_waveform(waveform)
@@ -275,8 +274,6 @@ def collate_fn(items: List[BatchItem]) -> Batch:
 
     target_texts = []
     audio_paths = []
-    
-
     for i, item in enumerate(items):
         waveform_len = item['waveform'].shape[0]
         target_len = len(item['target_ids'])
@@ -299,10 +296,10 @@ def collate_fn(items: List[BatchItem]) -> Batch:
     )
 
 
-def create_dataloader(tsv_path: Path, audio_dir: Path, noise_dir: Path, corridor_noise_dir: Path, tokenizer: ASRTokenizer, batch_size: int = 8, shuffle: bool = True, augment: bool = True, augment_prob: int = 0.5) -> DataLoader:
+def create_dataloader(tsv_path: Path, audio_dir: Path, noise_dir: Path, corridor_noise_dir: Path, tokenizer: ASRTokenizer, batch_size: int = 8, shuffle: bool = True, augment: bool = True, augment_prob: float = 0.5) -> DataLoader:
     dataset = CommonVoiceDataset(tsv_path=tsv_path, audio_dir=audio_dir, noise_dir=noise_dir, corridor_noise_dir=corridor_noise_dir, tokenizer=tokenizer, augment=augment, augment_prob=augment_prob)
 
-    return DataLoader(dataset=dataset, batch_size=batch_size, shuffle=shuffle, collate_fn=collate_fn, pin_memory=True, num_workers=8, prefetch_factor=8, persistent_workers=True)
+    return DataLoader(dataset=dataset, batch_size=batch_size, shuffle=shuffle, collate_fn=collate_fn, pin_memory=True, num_workers=8, prefetch_factor=8, persistent_workers=False)
 
 
 # ============ 测试代码 ============
@@ -327,9 +324,9 @@ if __name__ == "__main__":
     print("测试数据加载:")
     for batch in dataloader:
         batch: Batch
-        print(f"Mel specs shape: {batch['mel_specs'].shape}")
+        print(f"Waveform specs shape: {batch['targets'].shape}")
         print(f"Targets shape: {batch['targets'].shape}")
-        print(f"Mel lengths: {batch['mel_lengths']}")
+        print(f"Waveform lengths: {batch['waveform_lengths']}")
         print(f"Target lengths: {batch['target_lengths']}")
         print(f"Target texts: {batch['target_texts']}")
         print(f"Audio paths: {batch['audio_paths']}")

src/handle/audio_analyze.py

@@ -35,7 +35,7 @@ def analyze_mp3_files(directory: Path, ):
     """分析目录中的所有MP3文件"""
     results = []
     total_duration = 0
-    df = pd.read_csv(workspace_dir / '.data/ug/val_new.tsv', sep='\t')
+    df = pd.read_csv(workspace_dir / '.data/ug/validated.tsv', sep='\t')
 
     for _, row in tqdm(df.iterrows(), total=len(df), desc="anlayze audio"):
         mp3_file: Path = workspace_dir / '.data/ug/clips'  / row['path']

src/handle/export_model_state_dict.py

@@ -1,4 +1,5 @@
 
+import json
 from pathlib import Path
 
 import torch
@@ -6,8 +7,47 @@ import torch
 device = "cuda:0"
 workspace_dir = Path(__file__).parent.parent.parent
 
-input_checkpoint = workspace_dir.joinpath('.checkpoints/best_wer_model.pt')
-output_checkpoint = workspace_dir.joinpath('.checkpoints/prodect_best_wer_model.pt')
+input_checkpoint = workspace_dir.joinpath('.checkpoints/base_model/checkpoint_epoch_26.pt')
+output_checkpoint = workspace_dir.joinpath('.checkpoints/retrain.pt')
 
 checkpoint = torch.load(input_checkpoint, map_location=device)
-torch.save(checkpoint['model_state_dict'], output_checkpoint)
+
+checkpoint = {
+    'model_state_dict': checkpoint['model_state_dict'],
+    # 'optimizer_state_dict': checkpoint['optimizer_state_dict'],
+}
+torch.save(checkpoint, output_checkpoint)
+
+# torch.save(checkpoint['model_state_dict'], output_checkpoint)
+
+
+def query_model_parameter():
+    base_dir = workspace_dir / '.checkpoints/base_model'
+    checkpoints_info = []
+
+    for ckpt_path in sorted(base_dir.glob('checkpoint_epoch_*.pt'), key=lambda p: int(p.stem.split('_')[-1])):
+        ckpt = torch.load(ckpt_path, weights_only=False, map_location='cuda:1')
+        info = {
+            "file_name": ckpt_path.name,
+            "epoch": ckpt.get("epoch", "N/A"),
+            "train_loss": ckpt.get("train_loss", "N/A"),
+            "val_loss": ckpt.get("val_loss", "N/A"),
+            "cer": ckpt.get("cer", "N/A"),
+            "wer": ckpt.get("wer", "N/A"),
+            "has_scheduler": "scheduler_state_dict" in ckpt
+        }
+        checkpoints_info.append(info)
+        print(f'{ckpt_path.name}:')
+        print(f'  Epoch: {ckpt.get("epoch", "N/A")}')
+        print(f'  Train Loss: {ckpt.get("train_loss", "N/A")}')
+        print(f'  Val Loss: {ckpt.get("val_loss", "N/A")}')
+        print(f'  CER: {ckpt.get("cer", "N/A")}')
+        print(f'  WER: {ckpt.get("wer", "N/A")}')
+        print(f'  Has scheduler: {"scheduler_state_dict" in ckpt}')
+        print()
+
+    output_path = base_dir / 'checkpoints_summary.json'
+    with open(output_path, 'w', encoding='utf-8') as f:
+        json.dump(checkpoints_info, f, ensure_ascii=False, indent=2)
+
+# query_model_parameter()

src/handle/export_tokens.py

@@ -0,0 +1,38 @@
+
+from pathlib import Path
+import sys
+
+from tqdm import tqdm
+
+from text_normalizer import collapse_spaces
+
+
+
+workspace_dir = Path(__file__).parent.parent.parent
+sys.path.append(str(workspace_dir.joinpath('src')))
+# sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from handle.text_handle import process_text
+from tokenizer import ASRTokenizer
+
+# text = "ئىكەنلىكىنى، بۈگۈنكىسى 20 يىللىق ساقچىلىق جەريانىدا تۇنجى"
+# text = "	يېزىدىكى كەڭ، ئازادە ئۆي، باغلىرىنى تاشلاپ، خەقنىڭ ھويلىسىدا قورۇنۇپ-ئەيمىنىپ يەر دەسسەپ يۈردى.	"
+# text = "بۇ يىللىق ئەمگەكچىلەر بايرىمىدا، 1-مايدىن 5-مايغىچە جەمئىي بەش كۈن دەم ئېلىشقا قويۇپ بېرىلىدۇ. 9-ماي (شەنبە) نورمال خىزمەت قىلىنىدۇ. شۇنىڭ بىلەن بىر ۋاقىتتا، ئۈرۈمچى شەھىرى تىيانشان رايونى، سايباغ رايونى، داۋانچىڭ رايونى، ئۈرۈمچى ناھىيەسىدىكى ئوتتۇرا، باشلانغۇچ مەكتەپلەر 29-ئاپرېلدىن 30-ئاپرېلغىچە ئەتىيازلىق دەم ئېلىشقا قويۇپ بېرىدۇ؛ سانجى ئوبلاستىدىكى ئوتتۇرا، باشلانغۇچ مەكتەپلەرنىڭ ئەتىيازلىق دەم ئېلىشى ئۈچ كۈن بولۇپ، بۇنىڭ ئىچىدە ئىككى كۈن 29-، 30-ئاپرېلغا ئورۇنلاشتۇرۇلىدۇ، قالغان بىر كۈن 1-ئىيۇنغا ئورۇنلاشتۇرۇلىدۇ، قۇربان ھېيتلىق دەم ئېلىش بىلەن بىرلەشتۈرۈلۈپ، ئۇدا ئالتە كۈن دەم ئېلىنىدۇ."
+# text = "ئەڭ يېڭى دەم ئېلىشقا قويۇپ بېرىش ئۇقتۇرۇشى! تەقەززالىق بىلەن كۈتكەن يەنە بىر دەم ئېلىش كېلەي دەپ قالدى! گوۋۇيۈەن بەنگۇڭتىڭىنىڭ «2025-يىللىق قىسمەن بايرام، دەم ئېلىش كۈنلىرىنى ئورۇنلاشتۇرۇش توغرىسىدىكى ئۇقتۇرۇشى»غا ئاساسەن، دۈەنۋۇ بايرىمىلىق دەم ئېلىشقا قويۇپ بېرىش ئورۇنلاشتۇرۇلۇشى تۆۋەندىكىچە: 5-ئاينىڭ 31-كۈنى (شەنبە)دىن 6-ئاينىڭ 2-كۈنىگىچە جەمئىي ئۈچ كۈن دەم ئېلىشقا قويۇپ بېرىلىدۇ! بۇ قېتىمقى دۈەنۋۇ بايرىمىلىق دەم ئېلىش ۋاقتى تەڭشەلمەيدۇ!"
+# text = "مەن شۇ چاغدا چۈشۈمدىمۇ كۆرۈپ باقمىغان مۇنچىۋالا جىق قېرىنداشلىرىم، جىگەرلىرىمنىڭ بارلىقىدىن سۆيۈندۈم."
+# text = "ئامېرىكا جاھان گىرلىكىگە قارشى تۇرۇپ، چاۋشەنگە ياردەم بىرىش ئۇرۇشىنىڭ ئۇلۇغ غەلبىسى، جۇڭگو خەلقى ئورۇندىن دەس تۇرغاندىنكىن دۇنيانىڭ شەرقىدە قەد       كۆتۈرگەنلىكىنىڭ خىتاپنامىس"
+
+tokenizer = ASRTokenizer(vocab_path=workspace_dir / 'config/asr_vocab.json')
+
+# result = export_syllabize(text)
+# result = process_text(text)
+# print(f"Original: {text}")
+# print(f"Syllables: {result}")
+
+with open('/mnt/train/nmt/data/temp/uig_Arab.txt', 'r', encoding='utf-8') as f:
+    with open('/mnt/train/nmt/data/temp/uig_Arab_tokenized.txt', 'w', encoding='utf-8') as w:
+        lines = f.readlines()
+        for line in tqdm(lines):
+            w.write(collapse_spaces(" ".join([tokenizer.decode([t]).replace(" ", "<SPACE>") for t in tokenizer.encode(line.strip())])) + '\n')
+
+# ids = tokenizer.encode(text=text)
+# print(' '.join(tokenizer.decode([id]) for id in ids))

src/handle/test.py

@@ -11,11 +11,12 @@ from tqdm import tqdm
 workspace_dir = Path(__file__).parent.parent.parent
 sys.path.append(str(workspace_dir.joinpath('src')))
 # sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from handle.text_normalizer import clean_input_text
 from handle.text_handle import process_text
 from tokenizer import ASRTokenizer
 
 # 读取 TSV
-# df = pd.read_csv(workspace_dir / '.data/ug/train.tsv', sep='\t')
+# df = pd.read_csv(workspace_dir / '.data/ug/validated.tsv', sep='\t')
 
 # # 计算每个音频的时长（秒）
 # durations = []
@@ -36,12 +37,13 @@ from tokenizer import ASRTokenizer
 # print(df['duration'].describe())
 # print(f"超过20秒的样本: {(df['duration'] > 20).sum()}")
 
-# 保存结果（可选）
+# # 保存结果（可选）
 # df.to_csv('data/audio/train_with_duration.tsv', sep='\t', index=False)
 
 # audio_path = workspace_dir / '.data/ug/clips/common_voice_ug_40252722.mp3'
 # info = torchaudio.info(audio_path)
 # print("info:", info)
+
 import pandas as pd
 import torch
 import torchaudio
@@ -197,7 +199,7 @@ text = "\" 15 يىل بۇرۇن مەن بىلەن سەي چۇڭشىن (ئال
 text = "يۆ جۇڭمىڭ مۇنداق دېدى: 2019- يىلى 12-ئايدا، مەملىكەتلىك خەلق قۇرۇلتىيى دائىمىي كومىتېتىنىڭ 44- قېتىملىق كومىتېت باشلىقلىرى يىغىنى مەملىكەتلىك خەلق قۇرۇلتىيى دائىمىي كومىتېتىنىڭ 2020-يىللىق قانۇن چىقىرىش خىزمىتى پىلانىنى پىرىنسىپ جەھەتتىن ماقۇللىدى، خىزمەت تەرتىپى بويىچە، 13-نۆۋەتلىك مەملىكەتلىك خەلق قۇرۇلتىيى 3-يىغىنىنىڭ روھى ۋە ۋەكىللەرنىڭ تەكلىپ-تەۋسىيەلىرىگە ئاساسەن پىلاننى تەڭشەش كېرەك. بۇ يىل 6-ئاينىڭ 1-كۈنى، 58-قېتىملىق كومىتېت باشلىقلىرى يىغىنى تەڭشەلگەندىن كېيىنكى يىللىق قانۇن چىقىرىش خىزمىتى پىلانىنى قاراپ چىقىپ ماقۇللىدى."
 text = "	ئاشقازان-ئۈچەينىڭ لۆمۈلدىشىنى ئىلگىرى سۈرىدىغان دورىنى تاماقتىن بۇرۇن ئىستېمال قىلىش كېرەك."
 text = "مەن مەكتەپكە باردىم"
-# text = "غەرىپئەللىرى"
+text = "غەرىپئەللىرى"
 # text = "ئىكەنلىكىنى، بۈگۈنكىسى 20 يىللىق ساقچىلىق جەريانىدا تۇنجى"
 # text = "	يېزىدىكى كەڭ، ئازادە ئۆي، باغلىرىنى تاشلاپ، خەقنىڭ ھويلىسىدا قورۇنۇپ-ئەيمىنىپ يەر دەسسەپ يۈردى.	"
 # text = "بۇ يىللىق ئەمگەكچىلەر بايرىمىدا، 1-مايدىن 5-مايغىچە جەمئىي بەش كۈن دەم ئېلىشقا قويۇپ بېرىلىدۇ. 9-ماي (شەنبە) نورمال خىزمەت قىلىنىدۇ. شۇنىڭ بىلەن بىر ۋاقىتتا، ئۈرۈمچى شەھىرى تىيانشان رايونى، سايباغ رايونى، داۋانچىڭ رايونى، ئۈرۈمچى ناھىيەسىدىكى ئوتتۇرا، باشلانغۇچ مەكتەپلەر 29-ئاپرېلدىن 30-ئاپرېلغىچە ئەتىيازلىق دەم ئېلىشقا قويۇپ بېرىدۇ؛ سانجى ئوبلاستىدىكى ئوتتۇرا، باشلانغۇچ مەكتەپلەرنىڭ ئەتىيازلىق دەم ئېلىشى ئۈچ كۈن بولۇپ، بۇنىڭ ئىچىدە ئىككى كۈن 29-، 30-ئاپرېلغا ئورۇنلاشتۇرۇلىدۇ، قالغان بىر كۈن 1-ئىيۇنغا ئورۇنلاشتۇرۇلىدۇ، قۇربان ھېيتلىق دەم ئېلىش بىلەن بىرلەشتۈرۈلۈپ، ئۇدا ئالتە كۈن دەم ئېلىنىدۇ."
@@ -207,14 +209,32 @@ text = "مەن مەكتەپكە باردىم"
 
 
 # result = export_syllabize(text)
-result = process_text(text)
-print(f"Original: {text}")
-print(f"Syllables: {result}")
+# result = process_text(text)
+# print(f"Original: {text}")
+# print(f"Syllables: {result}")
 
 
-# tokenizer = ASRTokenizer(vocab_path=workspace_dir / 'config/asr_vocab.json')
+tokenizer = ASRTokenizer(vocab_path=workspace_dir / 'config/asr_vocab.json')
 
-# print(text)
-# ids = tokenizer.encode(text=text)
-# # print(ids)
-# print('|'.join(tokenizer.decode([id]) for id in ids))
+def static_token(tsv_path: Path):
+    df = pd.read_csv(tsv_path, sep='\t')
+    unk_samples = []
+    # for _, row in tqdm(df.iterrows(), total=len(df), desc="anlayze token"):
+    for sentence in tqdm(df['sentence'], total=len(df), desc="analyze token"):
+        text = clean_input_text(sentence.strip())
+        encoded = tokenizer.encode(text=text)
+        decoded = tokenizer.decode(encoded)
+        if "<UNK>" in decoded:
+            unk_samples.append({
+                # 'original': sentence.strip(),
+                'cleaned': text,
+                'decoded': decoded,
+                # 'encoded': encoded
+            })
+    
+    print(f"UNK samples: {len(unk_samples)} / {len(df)}")
+    return unk_samples
+
+tsv_path = workspace_dir / '.data/ug/train_new.tsv'
+unk_samples = static_token(tsv_path=tsv_path)
+# print(unk_samples)

src/handle/text_handle.py

@@ -1,5 +1,10 @@
 
-from .text_normalizer import UYGHUR_LETTERS, clean_input_text
+from pathlib import Path
+
+import pandas as pd
+from tqdm import tqdm
+
+from .text_normalizer import UYGHUR_LETTERS, clean_input_text, collapse_spaces, normalize_extended_uyghur_characters
 
 # def uighur_syllabize(text: str):
 #     # Uyghur Vowels
@@ -163,15 +168,13 @@ def uighur_syllabize(text: str):
 
 text = "	ھەر قانداق ئىشتا كىشىلەر بىلەن كېڭىشىش كېرەك. لېكىن كۆڭۈل تارتقان ئىشنى قىلىۋېرىش كېرەك."
 text = "\" 15 يىل بۇرۇن مەن بىلەن سەي چۇڭشىن (ئالى بابانىڭ قۇرغۇچىسىدىن بىرى) ئامېرىكىغا بېرىپ 30 نەچچە مەبلەغ سالغۇچى شىركەتلەر بىلەن كۆرۈشكىنىمىزدە ئۇلار بىزنى رەت قىلىپ ئىشىك سىرتىدا قالدۇرغاندى. ھېچكىم بىزگە ۋە كەلگۈسىمىزگە ئىشەنمىگەن ئىدى، ھېچكىممۇ بىزنىڭ ھازىر 300 مىليارد سودىنى تاماملىيالايدىغانلىقىمىزنى ئويلاپ خىيالىغىمۇ كەلتۈرمىگەن ئىدى.\" ئالى بابانىنىڭ ئورگىنى تەرىپىدىن ئىشلەنگەن ئىگىلىك تىكلەش ھۆججەتلىك فىلىمى \" بۇ چۈش ئەمەس\" نىڭ باش قىسمىدا مۇنداق بىر داڭلىق سۆز چىقىدۇ:\" مەن تاغدىن ئۆتكەن ۋاقتىمدا تاغ ماڭا گەپ قىلمىدى، مەن دېڭىزنى كېچىپ ئۆتكىنىمدە دېڭىز ماڭا گەپ قىلمىدى.\" بۇ فىلىمنىڭ قوشۇمچە ئىسمى بولسا \" مايۈن ۋە ئۇنىڭ مەڭگۈلۈك ' ياش ئالى'سى\" بولۇپ، ھەرخىل خەتەر ۋە قىيىنلىقنى بىرمۇ بىر يەڭگەن مايۈن ۋە ئۇنىڭ ئالى بابا قوشۇنىدىكى ھەمكارلاشقۇچىلىرىنىڭ قەيسەر كەچمىشى جانلىق بايان قىلىنغان."
-text = "يۆ جيەنتاۋ يېقىنقى بىر مەزگىلدە، خىزمەتداشلىرى بىلەن نۇرغۇن قىيىنچىلىققا ئۇچرىغان شوپۇرغا ياردەم قىلغانلىقىنى، شۇنداقلا يېمەكلىك ۋە سۇ يەتكۈزۈپ بەرگەنلىكىنى، ئەمما ئاياغ سوۋغا قىلىشى تۇنجى قېتىم ئىكەنلىكىنى، بۈگۈنكىسى 20 يىللىق ساقچىلىق جەريانىدا تۇنجى قېتىم شوپۇرغا ئاياغ سوۋغا قىلىشى ئىكەنلىكىنى ئېيتتى."
-text = "يۆ جۇڭمىڭ مۇنداق دېدى: 2019- يىلى 12-ئايدا، مەملىكەتلىك خەلق قۇرۇلتىيى دائىمىي كومىتېتىنىڭ 44- قېتىملىق كومىتېت باشلىقلىرى يىغىنى مەملىكەتلىك خەلق قۇرۇلتىيى دائىمىي كومىتېتىنىڭ 2020-يىللىق قانۇن چىقىرىش خىزمىتى پىلانىنى پىرىنسىپ جەھەتتىن ماقۇللىدى، خىزمەت تەرتىپى بويىچە، 13-نۆۋەتلىك مەملىكەتلىك خەلق قۇرۇلتىيى 3-يىغىنىنىڭ روھى ۋە ۋەكىللەرنىڭ تەكلىپ-تەۋسىيەلىرىگە ئاساسەن پىلاننى تەڭشەش كېرەك. بۇ يىل 6-ئاينىڭ 1-كۈنى، 58-قېتىملىق كومىتېت باشلىقلىرى يىغىنى تەڭشەلگەندىن كېيىنكى يىللىق قانۇن چىقىرىش خىزمىتى پىلانىنى قاراپ چىقىپ ماقۇللىدى."
-text = "مەن مەكتەپكە باردىم"
+# text = "شۇڭا ئۇ «ئورمان دوختۇرى» دەپ ئاتالغان."
+# text = "يۆ جيەنتاۋ يېقىنقى بىر مەزگىلدە، خىزمەتداشلىرى بىلەن نۇرغۇن قىيىنچىلىققا ئۇچرىغان شوپۇرغا ياردەم قىلغانلىقىنى، شۇنداقلا يېمەكلىك ۋە سۇ يەتكۈزۈپ بەرگەنلىكىنى، ئەمما ئاياغ سوۋغا قىلىشى تۇنجى قېتىم ئىكەنلىكىنى، بۈگۈنكىسى 20 يىللىق ساقچىلىق جەريانىدا تۇنجى قېتىم شوپۇرغا ئاياغ سوۋغا قىلىشى ئىكەنلىكىنى ئېيتتى."
+# text = "يۆ جۇڭمىڭ مۇنداق دېدى: 2019- يىلى 12-ئايدا، مەملىكەتلىك خەلق قۇرۇلتىيى دائىمىي كومىتېتىنىڭ 44- قېتىملىق كومىتېت باشلىقلىرى يىغىنى مەملىكەتلىك خەلق قۇرۇلتىيى دائىمىي كومىتېتىنىڭ 2020-يىللىق قانۇن چىقىرىش خىزمىتى پىلانىنى پىرىنسىپ جەھەتتىن ماقۇللىدى، خىزمەت تەرتىپى بويىچە، 13-نۆۋەتلىك مەملىكەتلىك خەلق قۇرۇلتىيى 3-يىغىنىنىڭ روھى ۋە ۋەكىللەرنىڭ تەكلىپ-تەۋسىيەلىرىگە ئاساسەن پىلاننى تەڭشەش كېرەك. بۇ يىل 6-ئاينىڭ 1-كۈنى، 58-قېتىملىق كومىتېت باشلىقلىرى يىغىنى تەڭشەلگەندىن كېيىنكى يىللىق قانۇن چىقىرىش خىزمىتى پىلانىنى قاراپ چىقىپ ماقۇللىدى."
+# text = "مەن مەكتەپكە باردىم"
 
-# text = normalize_extended_uyghur_characters(text=text)
-# print(clean_uyghur(text=text))
 
 def process_text(text: str) -> list[str]:
-    text = clean_input_text(text=text)
     result = []
     current_word_chars = []
     

src/handle/text_normalizer.py

@@ -122,5 +122,5 @@ def clean_input_text(text: str) -> str:
   text = collapse_spaces(text)
   text = clean_http_links(text)
   text = normalize_extended_uyghur_characters(text)
-  text = clean_unknown_symbols(text)
+  # text = clean_unknown_symbols(text)
   return text

src/inference.py

@@ -58,7 +58,8 @@ class ASRInference:
         results = []
 
         for audio_path in audio_paths:
-            text = self.transcribe(audio_path=audio_path)
+            waveform = self._load_audio(audio_path=audio_path)
+            text = self.transcribe(waveform=waveform)
             results.append(text)
 
         return results

src/tokenizer.py

@@ -62,8 +62,6 @@ class ASRTokenizer:
                 token_id = self.vocab_to_id.get(token)
                 if token_id is not None:
                     result.append(token_id)
-                elif token in UYGHUR_LETTERS:
-                    result.append(self.vocab_to_id.get(token, self.vocab_to_id['<UNK>']))
                 else:
                     sub_pieces = self._split_long_syllable(token)
                     for piece in sub_pieces:

src/train.py

@@ -1,7 +1,7 @@
 import math
 
 import torch
-from torch import Tensor, device, no_grad, cuda
+from torch import Tensor, device, inference_mode, cuda
 from torch.optim import AdamW, Optimizer
 from torch.optim.lr_scheduler import OneCycleLR
 from torch.nn import CTCLoss, utils
@@ -23,7 +23,7 @@ CONFIG = {
     
     # 训练配置
     'num_epochs': 50,
-    'learning_rate': 2e-4,
+    'learning_rate': 2e-5,
     'weight_decay': 1e-4,
     'grad_clip_norm': 1.0,
     
@@ -31,8 +31,8 @@ CONFIG = {
     'input_dim': 256,
     'num_heads': 8,
     'ffn_dim': 2048,
-    'num_layers': 6,
-    'dropout': 0.15,
+    'num_layers': 8,
+    'dropout': 0.1,
     
     # 保存和评估
     'early_stopping_patience': 10,
@@ -103,7 +103,7 @@ def train_one_epoch(model: ASRModel, dataloader: DataLoader, criterion: CTCLoss,
         with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
             log_probs, lengths = model(waveforms=waveforms, waveform_lengths=waveform_lengths)
             log_probs: Tensor
-            log_probs_ctc = log_probs.permute(1, 0, 2)
+            log_probs_ctc = log_probs.permute(1, 0, 2)  # [T, B, vocab_size]
             loss: Tensor = criterion(log_probs=log_probs_ctc, targets=targets, input_lengths=lengths, target_lengths=target_lengths)
 
         scaler.scale(loss).backward()
@@ -124,7 +124,7 @@ def train_one_epoch(model: ASRModel, dataloader: DataLoader, criterion: CTCLoss,
     train_avg_loss = total_train_loss / num_batches
     return train_avg_loss, global_step
 
-def validate(model: ASRModel, dataloader: DataLoader, criterion: CTCLoss, device: device, tokenizer: ASRTokenizer, writer: SummaryWriter, global_step: int) -> tuple[float, float, float]:
+def validate(model: ASRModel, dataloader: DataLoader, criterion: CTCLoss, device: device, tokenizer: ASRTokenizer, writer: SummaryWriter, epoch: int) -> tuple[float, float, float]:
     model.eval()
     total_loss = 0
     total_cer = 0
@@ -133,7 +133,7 @@ def validate(model: ASRModel, dataloader: DataLoader, criterion: CTCLoss, device
     num_batches = 0
     examples = []
 
-    with no_grad():
+    with inference_mode():
         progress_bar = tqdm(dataloader, desc="Validate", leave=False)
         for batch in progress_bar:
             batch: Batch
@@ -151,6 +151,7 @@ def validate(model: ASRModel, dataloader: DataLoader, criterion: CTCLoss, device
             total_loss += loss.item()
 
             for i in range(log_probs.shape[0]):
+                # 告诉长度：log_probs[i, :lengths[i]
                 pred_text = tokenizer.ctc_greedy_decode(log_probs=log_probs[i])
                 true_text = batch['target_texts'][i]
                 cer = calculate_cer(pred=pred_text, target=true_text)
@@ -169,7 +170,7 @@ def validate(model: ASRModel, dataloader: DataLoader, criterion: CTCLoss, device
     avg_wer = total_wer / num_samples
 
     for index, (true_text, pred_text, cer, wer) in enumerate(examples):
-        writer.add_text(f'Val/Example_{index}', f'True: {true_text}\nPred: {pred_text}\nCER: {cer:.4f} | WER: {wer:.4f}', global_step)
+        writer.add_text(f'Val/Example_{index}', f'True: {true_text}\nPred: {pred_text}\nCER: {cer:.4f} | WER: {wer:.4f}', epoch)
     
     model.train()
     return avg_loss, avg_cer, avg_wer
@@ -190,28 +191,31 @@ def save_checkpoint(model: ASRModel, optimizer: Optimizer, scheduler: OneCycleLR
     torch.save(checkpoint, save_path)
 
 def find_latest_checkpoint(checkpoint_dir: Path) -> Path | None:
-    checkpoints = sorted(checkpoint_dir.glob('checkpoint_epoch_*.pt'), key=lambda p: int(p.stem.split('_')[-1]))
+    # checkpoints = sorted(checkpoint_dir.glob('checkpoint_epoch_*.pt'), key=lambda p: int(p.stem.split('_')[-1]))
+    checkpoints = [checkpoint_dir.joinpath('retrain.pt')]
     return checkpoints[-1] if checkpoints else None
 
 def load_checkpoint(file_path: Path, model: ASRModel, optimizer: AdamW, scheduler: OneCycleLR):
     checkpoint = torch.load(file_path, weights_only=False)
     model.load_state_dict(checkpoint['model_state_dict'])
-    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
-    scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
+    # optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
+    # for param_group in optimizer.param_groups:
+    #     param_group['lr'] = 5e-5 
+    # scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
     
-    epoch = checkpoint['epoch']
-    global_step = checkpoint['global_step']
-    best_cer = checkpoint['cer']
-    best_wer = checkpoint['wer']
-    current_prob = checkpoint['current_prob']
-    return epoch, global_step, best_cer, best_wer, current_prob
+    # epoch = checkpoint['epoch']
+    # global_step = checkpoint['global_step']
+    # best_cer = checkpoint['cer']
+    # best_wer = checkpoint['wer']
+    # current_prob = checkpoint['current_prob']
+    # return epoch, global_step, best_cer, best_wer, current_prob
 
 def main():
     workspace_dir = Path(__file__).parent.parent
     device = torch.device('cuda:0')
     tokenizer = ASRTokenizer(workspace_dir / 'config/asr_vocab.json')
-    final_prob = 0.8
-    warmup_epochs = 12
+    final_prob = 0.5
+    warmup_epochs = 6
     current_prob = 0.0
 
     # ============ 创建数据加载器 ============
@@ -227,7 +231,7 @@ def main():
         augment_prob=current_prob
     )
     
-    val_loader_clean = create_dataloader(
+    val_loader = create_dataloader(
         tsv_path=workspace_dir / '.data/ug/val_new.tsv',
         audio_dir=workspace_dir / '.data/ug/clips',
         noise_dir='/mnt/dataset/dataset/audio/noise',
@@ -237,18 +241,6 @@ def main():
         shuffle=False,
         augment=False
     )
-
-    val_loader_noisy = create_dataloader(
-        tsv_path=workspace_dir / '.data/ug/val_new.tsv',
-        audio_dir=workspace_dir / '.data/ug/clips',
-        noise_dir='/mnt/dataset/dataset/audio/noise',
-        corridor_noise_dir= workspace_dir / 'data/corridor',
-        tokenizer=tokenizer,
-        batch_size=CONFIG['batch_size'],
-        shuffle=False,
-        augment=True,
-        augment_prob=0.5
-    )
     
     # ============ 初始化模型 ============
     model = ASRModel(
@@ -264,6 +256,17 @@ def main():
 
     criterion = CTCLoss(blank=tokenizer.get_special_token_id('<BLANK>'), zero_infinity=True)
     optimizer = AdamW(model.parameters(), lr=CONFIG['learning_rate'], weight_decay=CONFIG['weight_decay'], foreach=True)
+
+    checkpoint_dir = workspace_dir / '.checkpoints'
+    checkpoint_dir.mkdir(exist_ok=True)
+
+    best_cer = float('inf')
+    best_wer = float('inf')
+    patience_counter = 0
+    global_step = 0
+    start_epoch = 0
+
+    latest = find_latest_checkpoint(checkpoint_dir)
     
     # 使用 OneCycleLR：自动处理 warmup 和衰减
     scheduler = OneCycleLR(
@@ -271,13 +274,19 @@ def main():
         max_lr=CONFIG['learning_rate'],
         epochs=CONFIG['num_epochs'],
         steps_per_epoch=len(train_loader),
-        pct_start=0.2,
+        pct_start=0.1,
         anneal_strategy='cos',
-        div_factor=25.0,  # 初始 lr = max_lr / 10
-        final_div_factor=1e4,  # 最终 lr = max_lr / 10000
+        div_factor=10.0,
+        final_div_factor=1e4,
     )
     scaler = GradScaler()
-    
+    load_checkpoint(latest, model, optimizer, scheduler)
+    # if latest:
+    #     start_epoch, global_step, best_cer, best_wer, current_prob = load_checkpoint(latest, model, optimizer, scheduler)
+    #     print(f"✅ 恢复训练: 从 epoch={start_epoch} 开始, global_step={global_step}")
+    # else:
+    #     start_epoch = 0
+    #     print("🆕 从头开始训练\n")
     # ============ TensorBoard ============
     log_dir = workspace_dir / 'runs' / datetime.now().strftime('%Y%m%d_%H%M%S')
     writer = SummaryWriter(log_dir)
@@ -286,27 +295,12 @@ def main():
     writer.add_text('Config', config_text, 0)
     print(f"📊 TensorBoard 日志: {log_dir}")
 
-    checkpoint_dir = workspace_dir / '.checkpoints'
-    checkpoint_dir.mkdir(exist_ok=True)
-
     # ============ 训练循环 ============
-    best_cer = float('inf')
-    best_wer = float('inf')
-    patience_counter = 0
-    global_step = 0
-    start_epoch = 0
-
-    latest = find_latest_checkpoint(checkpoint_dir)
-    if latest:
-        start_epoch, global_step, best_cer, best_wer, current_prob = load_checkpoint(latest, model, optimizer, scheduler)
-        start_epoch += 1  # 从下一个 epoch 开始
-        print(f"✅ 恢复训练: 从 epoch={start_epoch} 开始, global_step={global_step}")
-    else:
-        start_epoch = 0
-        print("🆕 从头开始训练\n")
-
     for epoch in range(start_epoch, CONFIG['num_epochs']):
-        current_prob = final_prob * (1 - math.cos(math.pi * epoch / warmup_epochs)) / 2
+        epoch = epoch + 1
+        current_prob = final_prob * min(1.0, epoch / warmup_epochs)
+        train_loader.dataset.augment_prob = current_prob
+        print(f"📈 Epoch {epoch} | Augment Prob: {train_loader.dataset.augment_prob:.2f}")
         train_loss, global_step = train_one_epoch(
             model=model,
             dataloader=train_loader, 
@@ -320,26 +314,25 @@ def main():
             scaler=scaler,
         )
 
-        val_loss_c, val_cer_c, val_wer_c = validate(model=model, dataloader=val_loader_clean, criterion=criterion, device=device, tokenizer=tokenizer, writer=writer, global_step=global_step)
-        val_loss_n, val_cer_n, val_wer_n = validate(model=model, dataloader=val_loader_noisy, criterion=criterion, device=device, tokenizer=tokenizer, writer=writer, global_step=global_step)
-        print(f"\n📊 Step {global_step} | Clean Val Loss: {val_loss_c:.4f} | Clean Val CER: {val_cer_c:.4f} | Clean Val WER: {val_wer_c:.4f}")
+        val_loss, val_cer, val_wer = validate(model=model, dataloader=val_loader, criterion=criterion, device=device, tokenizer=tokenizer, writer=writer, epoch=epoch)
+        print(f"\n📊 Step {global_step} | Val Loss: {val_loss:.4f} | Val CER: {val_cer:.4f} | Val WER: {val_wer:.4f}")
         
         # 保存常规 checkpoint
         checkpoint_path = checkpoint_dir / f'checkpoint_epoch_{epoch}.pt'
-        save_checkpoint(model=model, optimizer=optimizer, scheduler=scheduler, epoch=epoch, global_step=global_step, train_loss=train_loss, val_loss=val_loss_c, cer=val_cer_c, wer=val_wer_c, current_prob=current_prob, save_path=checkpoint_path)
+        save_checkpoint(model=model, optimizer=optimizer, scheduler=scheduler, epoch=epoch, global_step=global_step, train_loss=train_loss, val_loss=val_loss, cer=val_cer, wer=val_wer, current_prob=current_prob, save_path=checkpoint_path)
         
         # 分别保存最佳 CER 和 WER 模型
         improved = False
-        if val_cer_c < best_cer:
-            best_cer = val_cer_c
+        if val_cer < best_cer:
+            best_cer = val_cer
             best_cer_path = checkpoint_dir / 'best_cer_model.pt'
-            save_checkpoint(model=model, optimizer=optimizer, scheduler=scheduler, epoch=epoch, global_step=global_step, train_loss=train_loss, val_loss=val_loss_c, cer=val_cer_c, wer=val_wer_c, current_prob=current_prob, save_path=best_cer_path)
+            save_checkpoint(model=model, optimizer=optimizer, scheduler=scheduler, epoch=epoch, global_step=global_step, train_loss=train_loss, val_loss=val_loss, cer=val_cer, wer=val_wer, current_prob=current_prob, save_path=best_cer_path)
             improved = True
         
-        if val_wer_c < best_wer:
-            best_wer = val_wer_c
+        if val_wer < best_wer:
+            best_wer = val_wer
             best_wer_path = checkpoint_dir / 'best_wer_model.pt'
-            save_checkpoint(model=model, optimizer=optimizer, scheduler=scheduler, epoch=epoch, global_step=global_step, train_loss=train_loss, val_loss=val_loss_c, cer=val_cer_c, wer=val_wer_c, current_prob=current_prob, save_path=best_wer_path)
+            save_checkpoint(model=model, optimizer=optimizer, scheduler=scheduler, epoch=epoch, global_step=global_step, train_loss=train_loss, val_loss=val_loss, cer=val_cer, wer=val_wer, current_prob=current_prob, save_path=best_wer_path)
             improved = True
         
         # Early Stopping 逻辑
@@ -357,15 +350,10 @@ def main():
         cuda.empty_cache()
 
         writer.add_scalar('Train/EpochLoss', train_loss, epoch)
-        writer.add_scalar('Val/EpochLoss', val_loss_c, epoch)
-        writer.add_scalar('Val/Loss', val_loss_c, global_step)
-        writer.add_scalar('Val/CER', val_cer_c, global_step)
-        writer.add_scalar('Val/WER', val_wer_c, global_step)
-        writer.add_scalar('Val_Noisy/EpochLoss', val_loss_n, epoch)
-        writer.add_scalar('Val_Noisy/Loss', val_loss_n, global_step)
-        writer.add_scalar('Val_Noisy/CER', val_cer_n, global_step)
-        writer.add_scalar('Val_Noisy/WER', val_wer_n, global_step)
-        print(f"✅ Epoch {epoch} 完成 | Train Avg Loss: {train_loss:.4f} | Val Avg Loss: {val_loss_c:.4f} | Best CER: {best_cer:.4f} | Best WER: {best_wer:.4f}\n")
+        writer.add_scalar('Val/EpochLoss', val_loss, epoch)
+        writer.add_scalar('Val/CER', val_cer, epoch)
+        writer.add_scalar('Val/WER', val_wer, epoch)
+        print(f"✅ Epoch {epoch} 完成 | Train Avg Loss: {train_loss:.4f} | Val Avg Loss: {val_loss:.4f} | Best CER: {best_cer:.4f} | Best WER: {best_wer:.4f}\n")
         
         # Early Stopping 检查
         if patience_counter >= CONFIG['early_stopping_patience']:

src/validate.py

@@ -0,0 +1,132 @@
+
+
+from email.headerregistry import DateHeader
+from pathlib import Path
+
+from torch import Tensor, inference_mode
+import torch
+from torch.nn import CTCLoss
+from tqdm import tqdm
+
+from dataset import Batch, create_dataloader
+from model import ASRModel
+from tokenizer import ASRTokenizer
+from train import calculate_cer, calculate_wer
+
+def validate(model: ASRModel, dataloader: DateHeader, criterion: CTCLoss, device: torch.device, tokenizer: ASRTokenizer) -> tuple[float, float, float]:
+    model.eval()
+    total_loss = 0
+    total_cer = 0
+    total_wer = 0
+    num_samples = 0
+    num_batches = 0
+    examples = []
+
+    with inference_mode():
+        progress_bar = tqdm(dataloader, desc="Validate", leave=False)
+        for batch in progress_bar:
+            batch: Batch
+            waveforms = batch['waveforms'].to(device)
+            targets = batch['targets'].to(device)
+            waveform_lengths = batch['waveform_lengths'].to(device)
+            target_lengths = batch['target_lengths'].to(device)
+
+            with torch.autocast(device_type='cuda', dtype=torch.float16):
+                log_probs, lengths = model(waveforms=waveforms, waveform_lengths=waveform_lengths)
+                log_probs: Tensor
+                log_probs_ctc = log_probs.permute(1, 0, 2)
+                loss: Tensor = criterion(log_probs=log_probs_ctc, targets=targets, input_lengths=lengths, target_lengths=target_lengths)
+            
+            total_loss += loss.item()
+            diff_count = 0
+            printed_diff = 0
+
+            for i in range(log_probs.shape[0]):
+                pred_full = tokenizer.ctc_greedy_decode(log_probs=log_probs[i])
+                pred_sliced = tokenizer.ctc_greedy_decode(log_probs=log_probs[i, :lengths[i]])
+
+                if pred_full != pred_sliced:
+                    diff_count += 1
+
+                    if printed_diff < 5:
+                        print("\nDECODE DIFF FOUND")
+                        print("audio:", batch["audio_paths"][i])
+                        print("waveform_length:", waveform_lengths[i].item())
+                        print("model_valid_length:", lengths[i].item())
+                        print("model_full_length:", log_probs.shape[1])
+                        print("target:", batch["target_texts"][i])
+                        print("full:", pred_full)
+                        print("sliced:", pred_sliced)
+                        printed_diff += 1
+
+                pred_text = pred_sliced
+                true_text = batch['target_texts'][i]
+                cer = calculate_cer(pred=pred_text, target=true_text)
+                wer = calculate_wer(pred=pred_text, target=true_text)
+                total_cer += cer
+                total_wer += wer
+                num_samples += 1
+
+                if len(examples) < 3:
+                    examples.append((true_text, pred_text, cer, wer))
+
+            num_batches += 1
+
+    avg_loss = total_loss / num_batches
+    avg_cer = total_cer / num_samples
+    avg_wer = total_wer / num_samples
+
+    return avg_loss, avg_cer, avg_wer
+
+
+CONFIG = {
+    # 数据配置
+    'batch_size': 32,
+    
+    # 训练配置
+    'num_epochs': 50,
+    'learning_rate': 2e-5,
+    'weight_decay': 1e-4,
+    'grad_clip_norm': 1.0,
+    
+    # 模型配置
+    'input_dim': 256,
+    'num_heads': 8,
+    'ffn_dim': 2048,
+    'num_layers': 8,
+    'dropout': 0.1,
+    
+    # 保存和评估
+    'early_stopping_patience': 10,
+}
+device = torch.device('cuda:1')
+workspace_dir = Path(__file__).parent.parent
+tokenizer = ASRTokenizer(workspace_dir / 'config/asr_vocab.json')
+model = ASRModel(
+    vocab_size=tokenizer.vocab_size(),
+    input_dim=CONFIG['input_dim'],
+    num_heads=CONFIG['num_heads'],
+    ffn_dim=CONFIG['ffn_dim'],
+    num_layers=CONFIG['num_layers'],
+    dropout=CONFIG['dropout'],
+).to(device)
+
+checkpoint = workspace_dir / ".checkpoints/checkpoint_epoch_12.pt"
+model.load_state_dict(torch.load(checkpoint, map_location=device)['model_state_dict'])
+print(f"🤖 模型参数量: {model.get_num_params() / 1e6:.2f}M")
+
+
+val_loader = create_dataloader(
+    tsv_path=workspace_dir / '.data/ug/val_new.tsv',
+    audio_dir=workspace_dir / '.data/ug/clips',
+    noise_dir='/mnt/dataset/dataset/audio/noise',
+    corridor_noise_dir= workspace_dir / 'data/corridor',
+    tokenizer=tokenizer,
+    batch_size=10,
+    shuffle=False,
+    augment=False
+)
+
+criterion = CTCLoss(blank=tokenizer.get_special_token_id('<BLANK>'), zero_infinity=True)
+val_loss, val_cer, val_wer = validate(model=model, dataloader=val_loader, criterion=criterion, device=device, tokenizer=tokenizer)
+print(f"\n📊 Val Loss: {val_loss:.4f} | Clean Val CER: {val_cer:.4f} | Clean Val WER: {val_wer:.4f}")

uv.lock

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+[[package]]
+name = "audio-model"
+version = "0.1.0"
+source = { virtual = "." }
+dependencies = [
+    { name = "ipython" },
+    { name = "librosa" },
+    { name = "matplotlib" },
+    { name = "mutagen" },
+    { name = "numpy" },
+    { name = "pandas" },
+    { name = "pillow" },
+    { name = "pydub" },
+    { name = "pyrubberband" },
+    { name = "setuptools" },
+    { name = "silero-vad" },
+    { name = "tensorboard" },
+    { name = "tensorboardx" },
+    { name = "torch" },
+    { name = "torch-audiomentations" },
+    { name = "torchaudio" },
+    { name = "torchcodec" },
+    { name = "tqdm" },
+    { name = "webrtcvad" },
+]
+
+[package.metadata]
+requires-dist = [
+    { name = "ipython", specifier = ">=9.10.1" },
+    { name = "librosa", specifier = ">=0.11.0" },
+    { name = "matplotlib", specifier = ">=3.10.8" },
+    { name = "mutagen", specifier = ">=1.47.0" },
+    { name = "numpy", specifier = ">=2.4.4" },
+    { name = "pandas", specifier = ">=3.0.2" },
+    { name = "pillow", specifier = ">=12.2.0" },
+    { name = "pydub", specifier = ">=0.25.1" },
+    { name = "pyrubberband", specifier = ">=0.4.0" },
+    { name = "setuptools", specifier = "<82" },
+    { name = "silero-vad", specifier = ">=6.2.1" },
+    { name = "tensorboard", specifier = ">=2.20.0" },
+    { name = "tensorboardx", specifier = ">=2.6.5" },
+    { name = "torch", specifier = "==2.8.0" },
+    { name = "torch-audiomentations", specifier = ">=0.12.0" },
+    { name = "torchaudio", specifier = "==2.8.0" },
+    { name = "torchcodec", specifier = "==0.7.0" },
+    { name = "tqdm", specifier = ">=4.67.3" },
+    { name = "webrtcvad", specifier = ">=2.0.10" },
+]
+
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 name = "audioop-lts"
 version = "0.2.2"
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