merge

.gitignore

@@ -159,10 +159,7 @@ Thumbs.db
 *.swp
 
 
-# project related
-data/Taxi1500*
 slurm_scripts/slurm_logs*
 # other
 temp
 .vscode
-.venv-3.10

scripts/variational_TTS_example.py

@@ -0,0 +1,79 @@
+"""
+    An example use of the transcription, translation and summarisation pipeline.
+"""
+import torch
+from datasets import Audio, load_dataset
+
+from arc_spice.dropout_utils.variational_inference import TTSVariationalPipeline
+
+
+def main(TTS_params):
+    """main function"""
+    var_pipe = TTSVariationalPipeline(TTS_params,n_variational_runs=2)
+
+    ds = load_dataset(
+        "facebook/multilingual_librispeech", "french", split="test", streaming=True
+    )
+    ds = ds.cast_column("audio", Audio(sampling_rate=16_000))
+    input_speech = next(iter(ds))["audio"]
+
+    var_pipe.clean_inference(input_speech["array"])
+    clean_output = var_pipe.clean_output
+
+    # logit shapes
+    print("\nLogit shapes:")
+    for step in var_pipe.pipeline_map.keys():
+        print(f"{step.capitalize()}: {clean_output[step]["logits"].shape}")
+
+    # entropy
+    print("\nMean entropy:")
+    for step in var_pipe.pipeline_map.keys():
+        print(f"{step.capitalize()}: {torch.mean(clean_output[step]["entropy"])}")
+
+    # normalised entropy
+    print("\nNormalised mean entropy:")
+    cumulative = 1
+    for step in var_pipe.pipeline_map.keys():
+        step_entropy = torch.mean(clean_output[step]["normalised_entropy"])
+        cumulative*= (1-step_entropy)
+        print(f"{step.capitalize()}: {step_entropy}")
+    print(f"Cumulative confidence (1 - entropy): {cumulative}")
+
+    # probabilities
+    print("\nMean top probabilities:")
+    cumulative = 1
+    for step in var_pipe.pipeline_map.keys():
+        step_prob = torch.mean(clean_output[step]["probs"])
+        cumulative *= step_prob
+        print(f"{step.capitalize()}: {step_prob}")
+    print(f"Cumulative confidence: {cumulative}")
+
+    print("\nConditional probabilities:")
+    for step in var_pipe.pipeline_map.keys():
+        token_probs = clean_output[step]["probs"]
+        cond_prob = torch.pow(torch.prod(token_probs,-1),1/len(token_probs))
+        print(f"{step.capitalize()}: {cond_prob}")
+
+    var_pipe.variational_inference(x=input_speech['array'])
+    variational_output = var_pipe.var_output
+    print("\nVariational Inference Semantic Density:")
+    for step in variational_output['variational'].keys():
+        print(f"{step}: {variational_output['variational'][step]['semantic_density']}")
+
+
+if __name__ == "__main__":
+    TTS_pars = {
+        "transcriber": {
+            "specific_task": "automatic-speech-recognition",
+            "model": "jonatasgrosman/wav2vec2-large-xlsr-53-french",
+        },
+        "translator": {
+            "specific_task": "translation_fr_to_en",
+            "model": "ybanas/autotrain-fr-en-translate-51410121895",
+        },
+        "summariser": {
+            "specific_task": "summarization",
+            "model": "marianna13/flan-t5-base-summarization",
+        },
+    }
+    main(TTS_params=TTS_pars)

src/arc_spice/dropout_utils/dropout_pipeline.py

@@ -0,0 +1,38 @@
+import torch
+from transformers import Pipeline, pipeline
+
+
+def set_dropout(model: torch.nn.Module, dropout_flag: bool) -> None:
+    """
+    Turn on or turn off dropout layers of a model.
+
+    Args:
+        model: pytorch model
+        dropout_flag: dropout -> True/False
+    """
+    for _, param in model.named_modules():
+        if isinstance(param, torch.nn.Dropout):
+            # dropout on (True) -> want training mode train(True)
+            # dropout off (False) -> eval mode train(False)
+            param.train(dropout_flag)
+
+
+def MCDropoutPipeline(task: str, model: str):
+    pl = pipeline(
+        task=task,
+        model=model,
+    )
+    initial_model = pl.model
+    pl.model = set_dropout(model=initial_model, dropout_flag=True)
+    return pl
+
+
+def test_dropout(pipe: Pipeline, dropout_flag: bool):
+    model = pipe.model
+    dropout_count = 0
+    for _, param in model.named_modules():
+        if isinstance(param, torch.nn.Dropout):
+            dropout_count += 1
+            assert param.training == dropout_flag
+
+    print(f"{dropout_count} dropout layers found in correct configuration.")