using monkey patch to replace models

egs/aishell/ASR/RESULTS.md

@@ -13,12 +13,13 @@
 
 Command for training is:
 ```bash
+pip install -r whisper/requirements.txt
+
 ./prepare.sh --stage 30 --stop_stage 30
 
 #fine-tuning with deepspeed zero stage 1
 torchrun --nproc-per-node 8 ./whisper/train.py \
   --max-duration 200 \
-  --use-fp16 1 \
   --exp-dir whisper/exp_large_v2 \
   --model-name large-v2 \
   --deepspeed \
@@ -27,21 +28,33 @@ torchrun --nproc-per-node 8 ./whisper/train.py \
 # fine-tuning with ddp
 torchrun --nproc-per-node 8 ./whisper/train.py \
   --max-duration 200 \
-  --use-fp16 1 \
   --exp-dir whisper/exp_medium \
   --base-lr 1e-5 \
   --model-name medium
 ```
 
-Command for decoding is:
+Command for decoding using fine-tuned models:
 ```bash
+git lfs install
+git clone https://huggingface.co/yuekai/icefall_asr_aishell_whisper
+ln -s icefall_asr_aishell_whisper/exp_large_v2/epoch-10-avg6.pt whisper/exp_large_v2/epoch-999.pt
+
 python3 ./whisper/decode.py \
   --exp-dir whisper/exp_large_v2 \
   --model-name large-v2 \
   --epoch 999 --avg 1 \
   --beam-size 10 --max-duration 50
 ```
-Pretrained models, training logs, decoding logs, tensorboard and decoding results
+Command for decoding using pretrained models (before fine-tuning):
+```bash
+python3 ./whisper/decode.py \
+  --exp-dir whisper/exp_large_v2 \
+  --model-name large-v2 \
+  --epoch -1 --avg 1 \
+  --remove-whisper-encoder-input-length-restriction False \
+  --beam-size 10 --max-duration 50
+```
+Fine-tuned models, training logs, decoding logs, tensorboard and decoding results
 are available at
 <https://huggingface.co/yuekai/icefall_asr_aishell_whisper>
 

egs/aishell/ASR/whisper/decode.py

@@ -16,6 +16,29 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+"""
+Usage:
+# Command for decoding using fine-tuned models:
+git lfs install
+git clone https://huggingface.co/yuekai/icefall_asr_aishell_whisper
+ln -s icefall_asr_aishell_whisper/exp_large_v2/epoch-10-avg6.pt whisper/exp_large_v2/epoch-999.pt
+
+python3 ./whisper/decode.py \
+  --exp-dir whisper/exp_large_v2 \
+  --model-name large-v2 \
+  --epoch 999 --avg 1 \
+  --beam-size 10 --max-duration 50
+
+# Command for decoding using pretrained models (before fine-tuning):
+
+python3 ./whisper/decode.py \
+  --exp-dir whisper/exp_large_v2 \
+  --model-name large-v2 \
+  --epoch -1 --avg 1 \
+  --remove-whisper-encoder-input-length-restriction False \
+  --beam-size 10 --max-duration 50
+
+"""
 
 import argparse
 import logging
@@ -29,8 +52,8 @@
 import torch
 import torch.nn as nn
 from asr_datamodule import AishellAsrDataModule
-from model import load_model
-
+#from model import load_model
+from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 from icefall.checkpoint import load_checkpoint, average_checkpoints_with_averaged_model
 from icefall.decode import (
     get_lattice,
@@ -104,7 +127,7 @@ def average_checkpoints(
 
 def remove_punctuation(text: str or List[str]):
   # https://github.com/yeyupiaoling/Whisper-Finetune/blob/master/utils/data_utils.py
-    punctuation = '!,.;:?、！，。；：？'
+    punctuation = '!,.;:?、！，。；：？《》 '
     if isinstance(text, str):
         text = re.sub(r'[{}]+'.format(punctuation), '', text).strip()
         return text
@@ -183,6 +206,13 @@ def get_parser():
         help="""The model name to use.
         """,
     )
+
+    parser.add_argument(
+        "--remove-whisper-encoder-input-length-restriction",
+        type=str2bool,
+        default=True,
+        help="replace whisper encoder forward method to remove input length restriction",
+    )
 
     return parser
 
@@ -246,6 +276,10 @@ def decode_one_batch(
     feature = batch["inputs"]
     assert feature.ndim == 3
     feature = feature.to(device, dtype=dtype).transpose(1, 2)
+    if not params.remove_whisper_encoder_input_length_restriction:
+        T = 3000
+        if feature.shape[2] < T:
+            feature = torch.cat([feature, torch.zeros(feature.shape[0], feature.shape[1], T - feature.shape[2]).to(device, dtype=dtype)], 2)
 
     supervisions = batch["supervisions"]
     feature_len = supervisions["num_frames"]
@@ -404,7 +438,9 @@ def main():
 
     logging.info(f"device: {device}")
 
-    model = load_model(params.model_name)
+    if params.remove_whisper_encoder_input_length_restriction:
+        replace_whisper_encoder_forward()
+    model = whisper.load_model(params.model_name, 'cpu')
     if params.epoch > 0:
       if params.avg > 1:
         start = params.epoch - params.avg

egs/aishell/ASR/whisper/requirements.txt

@@ -4,7 +4,7 @@ git+https://github.com/lhotse-speech/lhotse
 sentencepiece
 tensorboard
 librosa
-openai-whisper==20231117
+openai-whisper==git+https://github.com/yuekaizhang/whisper.git
 zhconv
 WeTextProcessing
 deepspeed

egs/aishell/ASR/whisper/train.py

@@ -17,20 +17,20 @@
 """
 Usage:
 
-./prepare.sh
-
-If you use --datatang-prob=0, then you don't need to run the above script.
-
-export CUDA_VISIBLE_DEVICES="0,1,2,3"
-
-./pruned_transducer_stateless7/train.py \
-  --world-size 4 \
-  --num-epochs 30 \
-  --start-epoch 1 \
-  --use-fp16 1 \
-  --exp-dir pruned_transducer_stateless7/exp \
-  --full-libri 1 \
-  --max-duration 550
+#fine-tuning with deepspeed zero stage 1
+torchrun --nproc-per-node 8 ./whisper/train.py \
+  --max-duration 200 \
+  --exp-dir whisper/exp_large_v2 \
+  --model-name large-v2 \
+  --deepspeed \
+  --deepspeed_config ./whisper/ds_config_zero1.json
+
+# fine-tuning with ddp
+torchrun --nproc-per-node 8 ./whisper/train.py \
+  --max-duration 200 \
+  --exp-dir whisper/exp_medium \
+  --base-lr 1e-5 \
+  --model-name medium
 """
 
 
@@ -88,7 +88,7 @@
 )
 
 import whisper
-from model import load_model
+from whisper_encoder_forward_monkey_patch import replace_whisper_encoder_forward
 from label_smoothing import LabelSmoothingLoss
 
 LRSchedulerType = Union[torch.optim.lr_scheduler._LRScheduler, optim.LRScheduler]
@@ -227,7 +227,7 @@ def get_parser():
     parser.add_argument(
         "--use-fp16",
         type=str2bool,
-        default=False,
+        default=True,
         help="Whether to use half precision training.",
     )
 
@@ -744,8 +744,9 @@ def run(rank, world_size, args):
     logging.info(params)
 
     logging.info("About to create model")
-
-    model = load_model(params.model_name)
+
+    replace_whisper_encoder_forward()
+    model = whisper.load_model(params.model_name, 'cpu')
     del model.alignment_heads
     num_param = sum([p.numel() for p in model.parameters()])
     logging.info(f"Number of model parameters: {num_param}")

egs/aishell/ASR/whisper/whisper_encoder_forward_monkey_patch.py

@@ -0,0 +1,26 @@
+import torch
+import whisper
+
+def forward(self, x: torch.Tensor):
+    """
+    x : torch.Tensor, shape = (batch_size, n_mels, n_ctx)
+        the mel spectrogram of the audio
+    """
+    x = F.gelu(self.conv1(x))
+    x = F.gelu(self.conv2(x))
+    x = x.permute(0, 2, 1)
+
+    x = (x + self.positional_embedding[:x.shape[1],:]).to(x.dtype)
+
+    for block in self.blocks:
+        x = block(x)
+
+    x = self.ln_post(x)
+    return x
+
+def replace_whisper_encoder_forward():
+    """
+    This function monkey patches the forward method of the whisper encoder.
+    To be called before the model is loaded, it changes whisper to process audio with any length < 30s.
+    """
+    whisper.model.AudioEncoder.forward = forward