diff --git a/users/vieting/experiments/librispeech/librispeech_960_pretraining/wav2vec2/config_02_fairseq_phoneme.py b/users/vieting/experiments/librispeech/librispeech_960_pretraining/wav2vec2/config_02_fairseq_phoneme.py
index c71bbb5c6..ce79cb86d 100644
--- a/users/vieting/experiments/librispeech/librispeech_960_pretraining/wav2vec2/config_02_fairseq_phoneme.py
+++ b/users/vieting/experiments/librispeech/librispeech_960_pretraining/wav2vec2/config_02_fairseq_phoneme.py
@@ -63,7 +63,7 @@ def get_fairseq_root(commit="e4a2e4e93efbcbaaae52a17ae6600beb2083fb33", fairseq_
     return fairseq_root
 
 
-def run_fairseq_pretraining(exp_name, commit, python_exe_hash_overwrite=None, **kwargs):
+def run_fairseq_pretraining(exp_name, commit, python_exe_hash_overwrite=None, checkpoint=None, **kwargs):
     """
     Runs a FairseqHydraTrainingJob to pretrain a wav2vec 2.0 model.
 
@@ -73,6 +73,8 @@ def run_fairseq_pretraining(exp_name, commit, python_exe_hash_overwrite=None, **
         python_exe_hash_overwrite (Optional[str]): The hash overwrite for the fairseq_python_exe to use.
             It should only be used to achieve compatibility with the previous setup structure and should be ignored
             in all other cases.
+        checkpoint (Optional[str]): The path to the checkpoint to start from. If None, the training will start
+            from scratch.
         **kwargs: Additional arguments to pass to the job. These will be used to overwrite the model configuration.
     """
     # job requirements
@@ -93,6 +95,8 @@ def run_fairseq_pretraining(exp_name, commit, python_exe_hash_overwrite=None, **
     # generate config
     fairseq_args = get_fairseq_args(num_gpus=num_gpus)
     fairseq_args["task"]["alignment"] = alignment
+    if checkpoint is not None:
+        fairseq_args["checkpoint"]["continue_once"] = checkpoint
     for k, v in kwargs.items():
         fairseq_args["model"][k] = v
     fairseq_config = FairseqHydraConfig(fairseq_args)
@@ -103,49 +107,6 @@ def run_fairseq_pretraining(exp_name, commit, python_exe_hash_overwrite=None, **
     tk.register_output(f"{prefix_name}/{exp_name}/pretraining/scores.png", job.out_plot_se)
     return job
 
-def run_fairseq_pretraining_from_checkpoint(exp_name, commit, checkpoint, python_exe_hash_overwrite=None, **kwargs):
-    """
-    Runs a FairseqHydraTrainingJob to pretrain a wav2vec 2.0 model.
-
-    Args:
-        exp_name (str): The name of the experiment, used for output and alias folder.
-        commit (str): The commit ID of the fairseq_phoneme repository to use.
-        checkpoint (str): The path to the checkpoint to start from.
-        python_exe_hash_overwrite (Optional[str]): The hash overwrite for the fairseq_python_exe to use.
-            It should only be used to achieve compatibility with the previous setup structure and should be ignored
-            in all other cases.
-        **kwargs: Additional arguments to pass to the job.
-    """
-    # job requirements
-    prefix_name = "experiments/librispeech/librispeech_960_pretraining/wav2vec2/"
-    alignment = get_alignment_hdf()
-    num_gpus = 8
-    fairseq_python_exe = tk.Path("/usr/bin/python3", hash_overwrite=python_exe_hash_overwrite)
-    fairseq_root = get_fairseq_root(fairseq_exe=fairseq_python_exe, commit=commit)
-    fairseq_training_args = dict(
-        save_interval=25,
-        max_epoch=600,
-        max_update=420000,
-        fairseq_root=fairseq_root,
-        fairseq_python_exe=fairseq_python_exe,
-        rqmt={"time": 336, "mem": 16, "cpu": 2, "gpu": num_gpus},
-    )
-
-    # generate config
-    fairseq_args = get_fairseq_args(num_gpus=num_gpus)
-    fairseq_args["task"]["alignment"] = alignment
-    fairseq_args["checkpoint"]["continue_once"] = checkpoint
-    for k, v in kwargs.items():
-        fairseq_args["model"][k] = v
-
-    fairseq_config = FairseqHydraConfig(fairseq_args)
-
-    # run pretraining
-    job = FairseqHydraTrainingJob(fairseq_config, **fairseq_training_args)
-    job.add_alias(os.path.join(prefix_name, exp_name, "pretraining"))
-    tk.register_output(f"{prefix_name}/{exp_name}/pretraining/scores.png", job.out_plot_se)
-    return job
-
 
 def py():
     # negatives other