add speech io dataset

egs/speech_llm/ASR_LLM/whisper_llm_zh/decode.py

@@ -206,10 +206,11 @@ def get_parser():
     )
 
     parser.add_argument(
-        "--use-aishell",
+        "--dataset",
-        type=str2bool,
+        type=str,
-        default=True,
+        default="aishell",
-        help="Whether to only use aishell1 dataset for training.",
+        choices=["aishell", "speechio", "wenetspeech_test_meeting", "multi_hans_zh"],
+        help="The dataset to decode",
     )
 
     add_model_arguments(parser)
@@ -540,7 +541,7 @@ def main():
 
 
     if params.avg > 1:
-        start = params.epoch - params.avg
+        start = params.epoch - params.avg + 1
         assert start >= 1, start
         checkpoint = torch.load(
             f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
@@ -551,18 +552,17 @@ def main():
             f"{params.exp_dir}/epoch-{epoch}.pt"
             for epoch in range(start, params.epoch + 1)
         ]
-        model.load_state_dict(average_checkpoints(filenames), strict=False)
+        avg_checkpoint = average_checkpoints(filenames)
+        model.load_state_dict(avg_checkpoint, strict=False)
 
         filename = f"{params.exp_dir}/epoch-{params.epoch}-avg-{params.avg}.pt"
-        torch.save(model.state_dict(), filename)
+        torch.save(avg_checkpoint, filename)
     else:
         checkpoint = torch.load(
             f"{params.exp_dir}/epoch-{params.epoch}.pt", map_location="cpu"
         )
-        if "model" not in checkpoint:
         model.load_state_dict(checkpoint, strict=False)
-        else:
+
-            load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
     model.to(device)
     model.eval()
     num_param = sum([p.numel() for p in model.parameters()])
@@ -584,11 +584,14 @@ def main():
             return False
         return True
 
-    if params.use_aishell:
+    if params.dataset == "aishell":
         test_sets_cuts = multi_dataset.aishell_test_cuts()
-    else:
+    elif params.dataset == "speechio":
-        # test_sets_cuts = multi_dataset.test_cuts()
+        test_sets_cuts = multi_dataset.speechio_test_cuts()
+    elif params.dataaset == "wenetspeech_test_meeting":
         test_sets_cuts = multi_dataset.wenetspeech_test_meeting_cuts()
+    else:
+        test_sets_cuts = multi_dataset.test_cuts()
 
     test_sets = test_sets_cuts.keys()
     test_dls = [

egs/speech_llm/ASR_LLM/whisper_llm_zh/multi_dataset.py

@@ -331,3 +331,25 @@ class MultiDataset:
         return {
             "wenetspeech-meeting_test": wenetspeech_test_meeting_cuts,
         }
+
+    def speechio_test_cuts(self) -> Dict[str, CutSet]:
+        logging.info("About to get multidataset test cuts")
+        start_index = 0
+        end_index = 26
+        dataset_parts = []
+        for i in range(start_index, end_index + 1):
+            idx = f"{i}".zfill(2)
+            dataset_parts.append(f"SPEECHIO_ASR_ZH000{idx}")
+
+        prefix = "speechio"
+        suffix = "jsonl.gz"
+
+        results_dict = {}
+        for partition in dataset_parts:
+            path = f"{prefix}_cuts_{partition}.{suffix}"
+
+            logging.info(f"Loading {path} set in lazy mode")
+            test_cuts = load_manifest_lazy(self.fbank_dir / path)
+            results_dict[partition] = test_cuts
+
+        return results_dict

egs/speech_llm/ASR_LLM/whisper_llm_zh/train.py

@@ -751,7 +751,6 @@ def run(rank, world_size, args):
     if params.pretrained_model_path:
         checkpoint = torch.load(params.pretrained_model_path, map_location="cpu")
         missing_keys, unexpected_keys = model.load_state_dict(checkpoint, strict=False)
-        assert len(unexpected_keys) == 0, unexpected_keys
 
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")