added new eval seen speaker dataset

Signed-off-by: Paarth Neekhara <[email protected]>

scripts/t5tts/infer_and_evaluate.py

@@ -40,6 +40,11 @@
         'audio_dir' : '/datap/misc/LibriTTSfromNemo/LibriTTS',
         'feature_dir' : '/datap/misc/LibriTTSfromNemo/LibriTTS',
     },
+    'libri_seen_test_v2': {
+        'manifest_path' : '/home/pneekhara/2023/SimpleT5NeMo/manifests/libri_seen_evalset_from_testclean_v2.json',
+        'audio_dir' : '/datap/misc/LibriTTSfromNemo/LibriTTS',
+        'feature_dir' : '/datap/misc/LibriTTSfromNemo/LibriTTS',
+    },
     'libri_unseen_val': {
         'manifest_path' : '/home/pneekhara/2023/SimpleT5NeMo/manifests/dev_clean_withContextAudioPaths_evalset.json',
         'audio_dir' : '/datap/misc/LibriTTSfromNemo/LibriTTS',
@@ -158,6 +163,11 @@ def run_inference(hparams_file, checkpoint_file, datasets, out_dir, temperature,
                     del dataset_meta_for_dl[key]
 
             dataset_meta = {dataset: dataset_meta_for_dl}
+            context_durration_min = model.cfg.get('context_duration_min', 5.0)
+            context_durration_max = model.cfg.get('context_duration_max', 5.0)
+            if context_durration_min < 5.0 and context_durration_max > 5.0:
+                context_durration_min = 5.0
+                context_durration_max = 5.0 # @pneekhara - For multiencoder models, I want fixed size contexts for fair eval. Not too important though.
             test_dataset = T5TTSDataset(
                 dataset_meta=dataset_meta,
                 sample_rate=model_cfg.sample_rate,
@@ -178,8 +188,8 @@ def run_inference(hparams_file, checkpoint_file, datasets, out_dir, temperature,
                 load_16khz_audio=model.model_type == 'single_encoder_sv_tts',
                 use_text_conditioning_tokenizer=model.use_text_conditioning_encoder,
                 pad_context_text_to_max_duration=model.pad_context_text_to_max_duration,
-                context_duration_min=model.cfg.get('context_duration_min', 5.0),
-                context_duration_max=model.cfg.get('context_duration_max', 5.0),
+                context_duration_min=context_durration_min,
+                context_duration_max=context_durration_max,
             )
             assert len(test_dataset) == len(manifest_records), "Dataset length and manifest length should be the same. Dataset length: {}, Manifest length: {}".format(len(test_dataset), len(manifest_records))
             test_dataset.text_tokenizer, test_dataset.text_conditioning_tokenizer = model._setup_tokenizers(model.cfg, mode='test')
@@ -263,7 +273,7 @@ def main():
     parser.add_argument('--hparams_files', type=str, default="/datap/misc/continuouscheckpoints_ks3ks3/multiencoder_small_sp_ks3_hparams.yaml,/datap/misc/continuouscheckpoints_ks3ks3/decodercontext_small_sp_ks3Correct_hparams.yaml")
     parser.add_argument('--checkpoint_files', type=str, default="/datap/misc/continuouscheckpoints_ks3ks3/multiencoder_small_sp_ks3_epoch302.ckpt,/datap/misc/continuouscheckpoints_ks3ks3/decodercontext_small_sp_ks3Correct_epoch305.ckpt")
     parser.add_argument('--codecmodel_path', type=str, default="/datap/misc/checkpoints/AudioCodec_21Hz_no_eliz.nemo")
-    parser.add_argument('--datasets', type=str, default="libri_unseen_test,libri_val")
+    parser.add_argument('--datasets', type=str, default="libri_seen_test,libri_unseen_test")
     parser.add_argument('--base_exp_dir', type=str, default="/datap/misc/eosmount4/AllKernselSize3/NewTransformer")
     parser.add_argument('--draco_exp_dir', type=str, default="/lustre/fsw/llmservice_nemo_speechlm/users/pneekhara/gitrepos/experiments/NewT5TTS_FixedPosEmb/AllKernselSize3/NewTransformer")
     parser.add_argument('--server_address', type=str, default="[email protected]")