remove invalid references to facebook/parler-tts-small

parler_tts/configuration_parler_tts.py

@@ -20,8 +20,8 @@
 
 logger = logging.get_logger(__name__)
 
-MUSICGEN_PRETRAINED_CONFIG_ARCHIVE_MAP = {
-    "facebook/parler_tts-small": "https://huggingface.co/facebook/parler_tts-small/resolve/main/config.json",
+PARLER_TTS_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "parler-tts/parler-tts-mini-v1": "https://huggingface.co/parler-tts/parler-tts-mini-v1/resolve/main/config.json",
     # See all ParlerTTS models at https://huggingface.co/models?filter=parler_tts
 }
 
@@ -31,7 +31,7 @@ class ParlerTTSDecoderConfig(PretrainedConfig):
     This is the configuration class to store the configuration of an [`ParlerTTSDecoder`]. It is used to instantiate a
     Parler-TTS decoder according to the specified arguments, defining the model architecture. Instantiating a
     configuration with the defaults will yield a similar configuration to that of the Parler-TTS
-    [facebook/parler_tts-small](https://huggingface.co/facebook/parler_tts-small) architecture.
+    [parler-tts/parler-tts-mini-v1](https://huggingface.co/parler-tts/parler-tts-mini-v1) architecture.
 
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information.
@@ -203,7 +203,7 @@ class ParlerTTSConfig(PretrainedConfig):
     ...     text_encoder_config, audio_encoder_config, decoder_config
     ... )
 
-    >>> # Initializing a ParlerTTSForConditionalGeneration (with random weights) from the facebook/parler_tts-small style configuration
+    >>> # Initializing a ParlerTTSForConditionalGeneration (with random weights) from the parler-tts/parler-tts-mini-v1 style configuration
     >>> model = ParlerTTSForConditionalGeneration(configuration)
 
     >>> # Accessing the model configuration

parler_tts/modeling_parler_tts.py

@@ -77,10 +77,10 @@
     logger.warn("Flash attention 2 is not installed")
 
 _CONFIG_FOR_DOC = "ParlerTTSConfig"
-_CHECKPOINT_FOR_DOC = "facebook/parler_tts-small"
+_CHECKPOINT_FOR_DOC = "parler-tts/parler-tts-mini-v1"
 
 MUSICGEN_PRETRAINED_MODEL_ARCHIVE_LIST = [
-    "facebook/parler_tts-small",
+    "parler-tts/parler-tts-mini-v1",
     # See all ParlerTTS models at https://huggingface.co/models?filter=parler_tts
 ]
 
@@ -2357,7 +2357,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
         ```python
         >>> from parler_tts import ParlerTTSForConditionalGeneration
 
-        >>> model = ParlerTTSForConditionalGeneration.from_pretrained("facebook/parler_tts-small")
+        >>> model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1")
         ```"""
 
         # At the moment fast initialization is not supported for composite models
@@ -2411,7 +2411,7 @@ def from_sub_models_pretrained(
 
                     - A string, the *model id* of a pretrained model hosted inside a model repo on huggingface.co.
                       Valid model ids can be located at the root-level, like `gpt2`, or namespaced under a user or
-                      organization name, like `facebook/parler_tts-small`.
+                      organization name, like `parler-tts/parler-tts-mini-v1`.
                     - A path to a *directory* containing model weights saved using
                       [`~PreTrainedModel.save_pretrained`], e.g., `./my_model_directory/`.
 
@@ -2440,7 +2440,7 @@ def from_sub_models_pretrained(
         >>> model = ParlerTTSForConditionalGeneration.from_sub_models_pretrained(
         ...     text_encoder_pretrained_model_name_or_path="t5-base",
         ...     audio_encoder_pretrained_model_name_or_path="facebook/encodec_24khz",
-        ...     decoder_pretrained_model_name_or_path="facebook/parler_tts-small",
+        ...     decoder_pretrained_model_name_or_path="parler-tts/parler-tts-mini-v1",
         ... )
         >>> # saving model after fine-tuning
         >>> model.save_pretrained("./parler_tts-ft")
@@ -2607,8 +2607,8 @@ def forward(
         >>> from transformers import AutoProcessor, ParlerTTSForConditionalGeneration
         >>> import torch
 
-        >>> processor = AutoProcessor.from_pretrained("facebook/parler_tts-small")
-        >>> model = ParlerTTSForConditionalGeneration.from_pretrained("facebook/parler_tts-small")
+        >>> processor = AutoProcessor.from_pretrained("parler-tts/parler-tts-mini-v1")
+        >>> model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler-tts-mini-v1")
 
         >>> inputs = processor(
         ...     text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],