big fix for speaker pipeline

egs_modelscope/asr_vad_spk/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/finetune.py

@@ -0,0 +1,35 @@
+import os
+from modelscope.metainfo import Trainers
+from modelscope.trainers import build_trainer
+from funasr.datasets.ms_dataset import MsDataset
+
+
+def modelscope_finetune(params):
+    if not os.path.exists(params.output_dir):
+        os.makedirs(params.output_dir, exist_ok=True)
+    # dataset split ["train", "validation"]
+    ds_dict = MsDataset.load(params.data_path)
+    kwargs = dict(
+        model=params.model,
+        model_revision=params.model_revision,
+        data_dir=ds_dict,
+        dataset_type=params.dataset_type,
+        work_dir=params.output_dir,
+        batch_bins=params.batch_bins,
+        max_epoch=params.max_epoch,
+        lr=params.lr)
+    trainer = build_trainer(Trainers.speech_asr_trainer, default_args=kwargs)
+    trainer.train()
+
+
+if __name__ == '__main__':
+    from funasr.utils.modelscope_param import modelscope_args
+    params = modelscope_args(model="damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn", data_path="./data")
+    params.output_dir = "./checkpoint"              # m模型保存路径
+    params.data_path = "./example_data/"            # 数据路径
+    params.dataset_type = "small"                   # 小数据量设置small，若数据量大于1000小时，请使用large
+    params.batch_bins = 2000                       # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒，
+    params.max_epoch = 50                           # 最大训练轮数
+    params.lr = 0.00005                             # 设置学习率
+    params.model_revision = "v1.2.1"
+    modelscope_finetune(params)

egs_modelscope/asr_vad_spk/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/infer.py

@@ -0,0 +1,27 @@
+import os
+import shutil
+import argparse
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+
+def modelscope_infer(args):
+    os.environ['CUDA_VISIBLE_DEVICES'] = str(args.gpuid)
+    inference_pipeline = pipeline(
+        task=Tasks.auto_speech_recognition,
+        model=args.model,
+        output_dir=args.output_dir,
+        param_dict={"decoding_model": args.decoding_mode, "hotword": args.hotword_txt}
+    )
+    inference_pipeline(audio_in=args.audio_in, batch_size_token=args.batch_size_token)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--model', type=str, default="damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn")
+    parser.add_argument('--audio_in', type=str, default="./data/test/wav.scp")
+    parser.add_argument('--output_dir', type=str, default="./results/")
+    parser.add_argument('--decoding_mode', type=str, default="normal")
+    parser.add_argument('--hotword_txt', type=str, default=None)
+    parser.add_argument('--batch_size_token', type=int, default=5000)
+    parser.add_argument('--gpuid', type=str, default="0")
+    args = parser.parse_args()
+    modelscope_infer(args)

funasr/bin/asr_inference_launch.py

@@ -55,6 +55,7 @@ from funasr.utils.speaker_utils import (check_audio_list,
                                         distribute_spk)
 from funasr.build_utils.build_model_from_file import build_model_from_file
 from funasr.utils.cluster_backend import ClusterBackend
+from funasr.utils.modelscope_utils import get_cache_dir
 from tqdm import tqdm
 
 def inference_asr(
@@ -791,7 +792,7 @@ def inference_paraformer_vad_speaker(
         time_stamp_writer: bool = True,
         punc_infer_config: Optional[str] = None,
         punc_model_file: Optional[str] = None,
-        sv_model_file: Optional[str] = "~/.cache/modelscope/hub/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/campplus_cn_common.bin",
+        sv_model_file: Optional[str] = None, 
         streaming: bool = False,
         embedding_node: str = "resnet1_dense",
         sv_threshold: float = 0.9465,
@@ -813,6 +814,9 @@ def inference_paraformer_vad_speaker(
         format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
     )
 
+    if sv_model_file is None:
+        sv_model_file = "{}/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/campplus_cn_common.bin".format(get_cache_dir(None))
+
     if param_dict is not None:
         hotword_list_or_file = param_dict.get('hotword')
     else:
@@ -937,7 +941,7 @@ def inference_paraformer_vad_speaker(
             #####  speaker_verification  #####
             ##################################
             # load sv model
-            sv_model_dict = torch.load(sv_model_file.replace("~", os.environ['HOME']), map_location=torch.device('cpu'))
+            sv_model_dict = torch.load(sv_model_file, map_location=torch.device('cpu'))
             sv_model = CAMPPlus()
             sv_model.load_state_dict(sv_model_dict)
             sv_model.eval()

funasr/utils/modelscope_utils.py

@@ -1,5 +1,6 @@
 import os
 from modelscope.hub.snapshot_download import snapshot_download
+from pathlib import Path
 
 
 def check_model_dir(model_dir, model_name: str = "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"):
@@ -13,4 +14,27 @@ def check_model_dir(model_dir, model_name: str = "damo/speech_fsmn_vad_zh-cn-16k
 	if not os.path.exists(dst):
 		os.symlink(model_dir, dst)
 	
-	model_dir = snapshot_download(model_name, cache_dir=dst_dir_root)
+	model_dir = snapshot_download(model_name, cache_dir=dst_dir_root)
+
+def get_default_cache_dir():
+    """
+    default base dir: '~/.cache/modelscope'
+    """
+    default_cache_dir = Path.home().joinpath('.cache', 'modelscope')
+    return default_cache_dir
+
+def get_cache_dir(model_id):
+    """cache dir precedence:
+        function parameter > environment > ~/.cache/modelscope/hub
+
+    Args:
+        model_id (str, optional): The model id.
+
+    Returns:
+        str: the model_id dir if model_id not None, otherwise cache root dir.
+    """
+    default_cache_dir = get_default_cache_dir()
+    base_path = os.getenv('MODELSCOPE_CACHE',
+                          os.path.join(default_cache_dir, 'hub'))
+    return base_path if model_id is None else os.path.join(
+        base_path, model_id + '/')