support using mini librispeech in training (#1048)

* support mini librispeech in training

* update onnx export doc

docs/source/model-export/export-onnx.rst

@@ -3,6 +3,15 @@ Export to ONNX
 
 In this section, we describe how to export models to `ONNX`_.
 
+.. hint::
+
+   Before you continue, please run:
+
+    .. code-block:: bash
+
+        pip install onnx
+
+
 In each recipe, there is a file called ``export-onnx.py``, which is used
 to export trained models to `ONNX`_.
 

egs/librispeech/ASR/pruned_transducer_stateless7/train.py

@@ -60,13 +60,13 @@ import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
 from asr_datamodule import LibriSpeechAsrDataModule
-from multidataset import MultiDataset
 from decoder import Decoder
 from joiner import Joiner
 from lhotse.cut import Cut
 from lhotse.dataset.sampling.base import CutSampler
 from lhotse.utils import fix_random_seed
 from model import Transducer
+from multidataset import MultiDataset
 from optim import Eden, ScaledAdam
 from torch import Tensor
 from torch.cuda.amp import GradScaler
@@ -1056,7 +1056,9 @@ def run(rank, world_size, args):
         multidataset = MultiDataset(params.manifest_dir, params.cv_manifest_dir)
         train_cuts = multidataset.train_cuts()
     else:
-        if params.full_libri:
+        if params.mini_libri:
+            train_cuts = librispeech.train_clean_5_cuts()
+        elif params.full_libri:
             train_cuts = librispeech.train_all_shuf_cuts()
         else:
             train_cuts = librispeech.train_clean_100_cuts()
@@ -1108,8 +1110,11 @@ def run(rank, world_size, args):
         train_cuts, sampler_state_dict=sampler_state_dict
     )
 
-    valid_cuts = librispeech.dev_clean_cuts()
+    if params.mini_libri:
-    valid_cuts += librispeech.dev_other_cuts()
+        valid_cuts = librispeech.dev_clean_2_cuts()
+    else:
+        valid_cuts = librispeech.dev_clean_cuts()
+        valid_cuts += librispeech.dev_other_cuts()
     valid_dl = librispeech.valid_dataloaders(valid_cuts)
 
     if not params.use_multidataset and not params.print_diagnostics:

egs/librispeech/ASR/pruned_transducer_stateless7_streaming/train.py

@@ -1049,10 +1049,13 @@ def run(rank, world_size, args):
 
     librispeech = LibriSpeechAsrDataModule(args)
 
-    train_cuts = librispeech.train_clean_100_cuts()
+    if params.mini_libri:
-    if params.full_libri:
+        train_cuts = librispeech.train_clean_5_cuts()
-        train_cuts += librispeech.train_clean_360_cuts()
+    else:
-        train_cuts += librispeech.train_other_500_cuts()
+        train_cuts = librispeech.train_clean_100_cuts()
+        if params.full_libri:
+            train_cuts += librispeech.train_clean_360_cuts()
+            train_cuts += librispeech.train_other_500_cuts()
 
     def remove_short_and_long_utt(c: Cut):
         # Keep only utterances with duration between 1 second and 20 seconds
@@ -1104,8 +1107,11 @@ def run(rank, world_size, args):
         train_cuts, sampler_state_dict=sampler_state_dict
     )
 
-    valid_cuts = librispeech.dev_clean_cuts()
+    if params.mini_libri:
-    valid_cuts += librispeech.dev_other_cuts()
+        valid_cuts = librispeech.dev_clean_2_cuts()
+    else:
+        valid_cuts = librispeech.dev_clean_cuts()
+        valid_cuts += librispeech.dev_other_cuts()
     valid_dl = librispeech.valid_dataloaders(valid_cuts)
 
     # if not params.print_diagnostics:

egs/librispeech/ASR/tdnn_lstm_ctc/asr_datamodule.py

@@ -86,8 +86,16 @@ class LibriSpeechAsrDataModule:
             "--full-libri",
             type=str2bool,
             default=True,
-            help="When enabled, use 960h LibriSpeech. Otherwise, use 100h subset.",
+            help="""Used only when --mini-libri is False.When enabled,
+            use 960h LibriSpeech. Otherwise, use 100h subset.""",
         )
+        group.add_argument(
+            "--mini-libri",
+            type=str2bool,
+            default=False,
+            help="True for mini librispeech",
+        )
+
         group.add_argument(
             "--manifest-dir",
             type=Path,
@@ -393,6 +401,13 @@ class LibriSpeechAsrDataModule:
         )
         return test_dl
 
+    @lru_cache()
+    def train_clean_5_cuts(self) -> CutSet:
+        logging.info("mini_librispeech: About to get train-clean-5 cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "librispeech_cuts_train-clean-5.jsonl.gz"
+        )
+
     @lru_cache()
     def train_clean_100_cuts(self) -> CutSet:
         logging.info("About to get train-clean-100 cuts")
@@ -424,6 +439,13 @@ class LibriSpeechAsrDataModule:
             self.args.manifest_dir / "librispeech_cuts_train-all-shuf.jsonl.gz"
         )
 
+    @lru_cache()
+    def dev_clean_2_cuts(self) -> CutSet:
+        logging.info("mini_librispeech: About to get dev-clean-2 cuts")
+        return load_manifest_lazy(
+            self.args.manifest_dir / "librispeech_cuts_dev-clean-2.jsonl.gz"
+        )
+
     @lru_cache()
     def dev_clean_cuts(self) -> CutSet:
         logging.info("About to get dev-clean cuts")