Add FastSpeech2Conformer (#23439)

* start - docs, SpeechT5 copy and rename

* add relevant code from FastSpeech2 draft, have tests pass

* make it an actual conformer, demo ex.

* matching inference with original repo, includes debug code

* refactor nn.Sequentials, start more desc. var names

* more renaming

* more renaming

* vocoder scratchwork

* matching vocoder outputs

* hifigan vocoder conversion script

* convert model script, rename some config vars

* replace postnet with speecht5's implementation

* passing common tests, file cleanup

* expand testing, add output hidden states and attention

* tokenizer + passing tokenizer tests

* variety of updates and tests

* g2p_en pckg setup

* import structure edits

* docstrings and cleanup

* repo consistency

* deps

* small cleanup

* forward signature param order

* address comments except for masks and labels

* address comments on attention_mask and labels

* address second round of comments

* remove old unneeded line

* address comments part 1

* address comments pt 2

* rename auto mapping

* fixes for failing tests

* address comments part 3 (bart-like, train loss)

* make style

* pass config where possible

* add forward method + tests to WithHifiGan model

* make style

* address arg passing and generate_speech comments

* address Arthur comments

* address Arthur comments pt2

* lint  changes

* Sanchit comment

* add g2p-en to doctest deps

* move up self.encoder

* onnx compatible tensor method

* fix is symbolic

* fix paper url

* move models to espnet org

* make style

* make fix-copies

* update docstring

* Arthur comments

* update docstring w/ new updates

* add model architecture images

* header size

* md wording update

* make style

src/transformers/models/fastspeech2_conformer/convert_fastspeech2_conformer_original_pytorch_checkpoint_to_pytorch.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+"""Convert FastSpeech2Conformer checkpoint."""
+
+import argparse
+import json
+import re
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import torch
+import yaml
+
+from transformers import (
+    FastSpeech2ConformerConfig,
+    FastSpeech2ConformerModel,
+    FastSpeech2ConformerTokenizer,
+    logging,
+)
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger("transformers.models.FastSpeech2Conformer")
+
+CONFIG_MAPPING = {
+    "adim": "hidden_size",
+    "aheads": "num_attention_heads",
+    "conformer_dec_kernel_size": "decoder_kernel_size",
+    "conformer_enc_kernel_size": "encoder_kernel_size",
+    "decoder_normalize_before": "decoder_normalize_before",
+    "dlayers": "decoder_layers",
+    "dunits": "decoder_linear_units",
+    "duration_predictor_chans": "duration_predictor_channels",
+    "duration_predictor_kernel_size": "duration_predictor_kernel_size",
+    "duration_predictor_layers": "duration_predictor_layers",
+    "elayers": "encoder_layers",
+    "encoder_normalize_before": "encoder_normalize_before",
+    "energy_embed_dropout": "energy_embed_dropout",
+    "energy_embed_kernel_size": "energy_embed_kernel_size",
+    "energy_predictor_chans": "energy_predictor_channels",
+    "energy_predictor_dropout": "energy_predictor_dropout",
+    "energy_predictor_kernel_size": "energy_predictor_kernel_size",
+    "energy_predictor_layers": "energy_predictor_layers",
+    "eunits": "encoder_linear_units",
+    "pitch_embed_dropout": "pitch_embed_dropout",
+    "pitch_embed_kernel_size": "pitch_embed_kernel_size",
+    "pitch_predictor_chans": "pitch_predictor_channels",
+    "pitch_predictor_dropout": "pitch_predictor_dropout",
+    "pitch_predictor_kernel_size": "pitch_predictor_kernel_size",
+    "pitch_predictor_layers": "pitch_predictor_layers",
+    "positionwise_conv_kernel_size": "positionwise_conv_kernel_size",
+    "postnet_chans": "speech_decoder_postnet_units",
+    "postnet_filts": "speech_decoder_postnet_kernel",
+    "postnet_layers": "speech_decoder_postnet_layers",
+    "reduction_factor": "reduction_factor",
+    "stop_gradient_from_energy_predictor": "stop_gradient_from_energy_predictor",
+    "stop_gradient_from_pitch_predictor": "stop_gradient_from_pitch_predictor",
+    "transformer_dec_attn_dropout_rate": "decoder_attention_dropout_rate",
+    "transformer_dec_dropout_rate": "decoder_dropout_rate",
+    "transformer_dec_positional_dropout_rate": "decoder_positional_dropout_rate",
+    "transformer_enc_attn_dropout_rate": "encoder_attention_dropout_rate",
+    "transformer_enc_dropout_rate": "encoder_dropout_rate",
+    "transformer_enc_positional_dropout_rate": "encoder_positional_dropout_rate",
+    "use_cnn_in_conformer": "use_cnn_in_conformer",
+    "use_macaron_style_in_conformer": "use_macaron_style_in_conformer",
+    "use_masking": "use_masking",
+    "use_weighted_masking": "use_weighted_masking",
+    "idim": "input_dim",
+    "odim": "num_mel_bins",
+    "spk_embed_dim": "speaker_embed_dim",
+    "langs": "num_languages",
+    "spks": "num_speakers",
+}
+
+
+def remap_model_yaml_config(yaml_config_path):
+    with Path(yaml_config_path).open("r", encoding="utf-8") as f:
+        args = yaml.safe_load(f)
+        args = argparse.Namespace(**args)
+
+    remapped_config = {}
+
+    model_params = args.tts_conf["text2mel_params"]
+    # espnet_config_key -> hf_config_key, any keys not included are ignored
+    for espnet_config_key, hf_config_key in CONFIG_MAPPING.items():
+        if espnet_config_key in model_params:
+            remapped_config[hf_config_key] = model_params[espnet_config_key]
+
+    return remapped_config, args.g2p, args.token_list
+
+
+def convert_espnet_state_dict_to_hf(state_dict):
+    new_state_dict = {}
+    for key in state_dict:
+        if "tts.generator.text2mel." in key:
+            new_key = key.replace("tts.generator.text2mel.", "")
+            if "postnet" in key:
+                new_key = new_key.replace("postnet.postnet", "speech_decoder_postnet.layers")
+                new_key = new_key.replace(".0.weight", ".conv.weight")
+                new_key = new_key.replace(".1.weight", ".batch_norm.weight")
+                new_key = new_key.replace(".1.bias", ".batch_norm.bias")
+                new_key = new_key.replace(".1.running_mean", ".batch_norm.running_mean")
+                new_key = new_key.replace(".1.running_var", ".batch_norm.running_var")
+                new_key = new_key.replace(".1.num_batches_tracked", ".batch_norm.num_batches_tracked")
+            if "feat_out" in key:
+                if "weight" in key:
+                    new_key = "speech_decoder_postnet.feat_out.weight"
+                if "bias" in key:
+                    new_key = "speech_decoder_postnet.feat_out.bias"
+            if "encoder.embed.0.weight" in key:
+                new_key = new_key.replace("0.", "")
+            if "w_1" in key:
+                new_key = new_key.replace("w_1", "conv1")
+            if "w_2" in key:
+                new_key = new_key.replace("w_2", "conv2")
+            if "predictor.conv" in key:
+                new_key = new_key.replace(".conv", ".conv_layers")
+                pattern = r"(\d)\.(\d)"
+                replacement = (
+                    r"\1.conv" if ("2.weight" not in new_key) and ("2.bias" not in new_key) else r"\1.layer_norm"
+                )
+                new_key = re.sub(pattern, replacement, new_key)
+            if "pitch_embed" in key or "energy_embed" in key:
+                new_key = new_key.replace("0", "conv")
+            if "encoders" in key:
+                new_key = new_key.replace("encoders", "conformer_layers")
+                new_key = new_key.replace("norm_final", "final_layer_norm")
+                new_key = new_key.replace("norm_mha", "self_attn_layer_norm")
+                new_key = new_key.replace("norm_ff_macaron", "ff_macaron_layer_norm")
+                new_key = new_key.replace("norm_ff", "ff_layer_norm")
+                new_key = new_key.replace("norm_conv", "conv_layer_norm")
+            if "lid_emb" in key:
+                new_key = new_key.replace("lid_emb", "language_id_embedding")
+            if "sid_emb" in key:
+                new_key = new_key.replace("sid_emb", "speaker_id_embedding")
+
+            new_state_dict[new_key] = state_dict[key]
+
+    return new_state_dict
+
+
+@torch.no_grad()
+def convert_FastSpeech2ConformerModel_checkpoint(
+    checkpoint_path,
+    yaml_config_path,
+    pytorch_dump_folder_path,
+    repo_id=None,
+):
+    model_params, tokenizer_name, vocab = remap_model_yaml_config(yaml_config_path)
+    config = FastSpeech2ConformerConfig(**model_params)
+
+    # Prepare the model
+    model = FastSpeech2ConformerModel(config)
+
+    espnet_checkpoint = torch.load(checkpoint_path)
+    hf_compatible_state_dict = convert_espnet_state_dict_to_hf(espnet_checkpoint)
+
+    model.load_state_dict(hf_compatible_state_dict)
+
+    model.save_pretrained(pytorch_dump_folder_path)
+
+    # Prepare the tokenizer
+    with TemporaryDirectory() as tempdir:
+        vocab = {token: id for id, token in enumerate(vocab)}
+        vocab_file = Path(tempdir) / "vocab.json"
+        with open(vocab_file, "w") as f:
+            json.dump(vocab, f)
+        should_strip_spaces = "no_space" in tokenizer_name
+        tokenizer = FastSpeech2ConformerTokenizer(str(vocab_file), should_strip_spaces=should_strip_spaces)
+
+    tokenizer.save_pretrained(pytorch_dump_folder_path)
+
+    if repo_id:
+        print("Pushing to the hub...")
+        model.push_to_hub(repo_id)
+        tokenizer.push_to_hub(repo_id)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
+    parser.add_argument(
+        "--yaml_config_path", required=True, default=None, type=str, help="Path to config.yaml of model to convert"
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
+    )
+    parser.add_argument(
+        "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
+    )
+
+    args = parser.parse_args()
+    convert_FastSpeech2ConformerModel_checkpoint(
+        args.checkpoint_path,
+        args.yaml_config_path,
+        args.pytorch_dump_folder_path,
+        args.push_to_hub,
+    )

src/transformers/models/fastspeech2_conformer/convert_hifigan.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+"""Convert FastSpeech2Conformer HiFi-GAN checkpoint."""
+
+import argparse
+from pathlib import Path
+
+import torch
+import yaml
+
+from transformers import FastSpeech2ConformerHifiGan, FastSpeech2ConformerHifiGanConfig, logging
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger("transformers.models.FastSpeech2Conformer")
+
+
+def load_weights(checkpoint, hf_model, config):
+    vocoder_key_prefix = "tts.generator.vocoder."
+    checkpoint = {k.replace(vocoder_key_prefix, ""): v for k, v in checkpoint.items() if vocoder_key_prefix in k}
+
+    hf_model.apply_weight_norm()
+
+    hf_model.conv_pre.weight_g.data = checkpoint["input_conv.weight_g"]
+    hf_model.conv_pre.weight_v.data = checkpoint["input_conv.weight_v"]
+    hf_model.conv_pre.bias.data = checkpoint["input_conv.bias"]
+
+    for i in range(len(config.upsample_rates)):
+        hf_model.upsampler[i].weight_g.data = checkpoint[f"upsamples.{i}.1.weight_g"]
+        hf_model.upsampler[i].weight_v.data = checkpoint[f"upsamples.{i}.1.weight_v"]
+        hf_model.upsampler[i].bias.data = checkpoint[f"upsamples.{i}.1.bias"]
+
+    for i in range(len(config.upsample_rates) * len(config.resblock_kernel_sizes)):
+        for j in range(len(config.resblock_dilation_sizes)):
+            hf_model.resblocks[i].convs1[j].weight_g.data = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_g"]
+            hf_model.resblocks[i].convs1[j].weight_v.data = checkpoint[f"blocks.{i}.convs1.{j}.1.weight_v"]
+            hf_model.resblocks[i].convs1[j].bias.data = checkpoint[f"blocks.{i}.convs1.{j}.1.bias"]
+
+            hf_model.resblocks[i].convs2[j].weight_g.data = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_g"]
+            hf_model.resblocks[i].convs2[j].weight_v.data = checkpoint[f"blocks.{i}.convs2.{j}.1.weight_v"]
+            hf_model.resblocks[i].convs2[j].bias.data = checkpoint[f"blocks.{i}.convs2.{j}.1.bias"]
+
+    hf_model.conv_post.weight_g.data = checkpoint["output_conv.1.weight_g"]
+    hf_model.conv_post.weight_v.data = checkpoint["output_conv.1.weight_v"]
+    hf_model.conv_post.bias.data = checkpoint["output_conv.1.bias"]
+
+    hf_model.remove_weight_norm()
+
+
+def remap_hifigan_yaml_config(yaml_config_path):
+    with Path(yaml_config_path).open("r", encoding="utf-8") as f:
+        args = yaml.safe_load(f)
+        args = argparse.Namespace(**args)
+
+    vocoder_type = args.tts_conf["vocoder_type"]
+    if vocoder_type != "hifigan_generator":
+        raise TypeError(f"Vocoder config must be for `hifigan_generator`, but got {vocoder_type}")
+
+    remapped_dict = {}
+    vocoder_params = args.tts_conf["vocoder_params"]
+
+    # espnet_config_key -> hf_config_key
+    key_mappings = {
+        "channels": "upsample_initial_channel",
+        "in_channels": "model_in_dim",
+        "resblock_dilations": "resblock_dilation_sizes",
+        "resblock_kernel_sizes": "resblock_kernel_sizes",
+        "upsample_kernel_sizes": "upsample_kernel_sizes",
+        "upsample_scales": "upsample_rates",
+    }
+    for espnet_config_key, hf_config_key in key_mappings.items():
+        remapped_dict[hf_config_key] = vocoder_params[espnet_config_key]
+    remapped_dict["sampling_rate"] = args.tts_conf["sampling_rate"]
+    remapped_dict["normalize_before"] = False
+    remapped_dict["leaky_relu_slope"] = vocoder_params["nonlinear_activation_params"]["negative_slope"]
+
+    return remapped_dict
+
+
+@torch.no_grad()
+def convert_hifigan_checkpoint(
+    checkpoint_path,
+    pytorch_dump_folder_path,
+    yaml_config_path=None,
+    repo_id=None,
+):
+    if yaml_config_path is not None:
+        config_kwargs = remap_hifigan_yaml_config(yaml_config_path)
+        config = FastSpeech2ConformerHifiGanConfig(**config_kwargs)
+    else:
+        config = FastSpeech2ConformerHifiGanConfig()
+
+    model = FastSpeech2ConformerHifiGan(config)
+
+    orig_checkpoint = torch.load(checkpoint_path)
+    load_weights(orig_checkpoint, model, config)
+
+    model.save_pretrained(pytorch_dump_folder_path)
+
+    if repo_id:
+        print("Pushing to the hub...")
+        model.push_to_hub(repo_id)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
+    parser.add_argument("--yaml_config_path", default=None, type=str, help="Path to config.yaml of model to convert")
+    parser.add_argument(
+        "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
+    )
+    parser.add_argument(
+        "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
+    )
+
+    args = parser.parse_args()
+    convert_hifigan_checkpoint(
+        args.checkpoint_path,
+        args.pytorch_dump_folder_path,
+        args.yaml_config_path,
+        args.push_to_hub,
+    )

src/transformers/models/fastspeech2_conformer/convert_model_with_hifigan.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+"""Convert FastSpeech2Conformer checkpoint."""
+
+import argparse
+
+import torch
+
+from transformers import (
+    FastSpeech2ConformerConfig,
+    FastSpeech2ConformerHifiGan,
+    FastSpeech2ConformerHifiGanConfig,
+    FastSpeech2ConformerModel,
+    FastSpeech2ConformerWithHifiGan,
+    FastSpeech2ConformerWithHifiGanConfig,
+    logging,
+)
+
+from .convert_fastspeech2_conformer_original_pytorch_checkpoint_to_pytorch import (
+    convert_espnet_state_dict_to_hf,
+    remap_model_yaml_config,
+)
+from .convert_hifigan import load_weights, remap_hifigan_yaml_config
+
+
+logging.set_verbosity_info()
+logger = logging.get_logger("transformers.models.FastSpeech2Conformer")
+
+
+def convert_FastSpeech2ConformerWithHifiGan_checkpoint(
+    checkpoint_path,
+    yaml_config_path,
+    pytorch_dump_folder_path,
+    repo_id=None,
+):
+    # Prepare the model
+    model_params, *_ = remap_model_yaml_config(yaml_config_path)
+    model_config = FastSpeech2ConformerConfig(**model_params)
+
+    model = FastSpeech2ConformerModel(model_config)
+
+    espnet_checkpoint = torch.load(checkpoint_path)
+    hf_compatible_state_dict = convert_espnet_state_dict_to_hf(espnet_checkpoint)
+    model.load_state_dict(hf_compatible_state_dict)
+
+    # Prepare the vocoder
+    config_kwargs = remap_hifigan_yaml_config(yaml_config_path)
+    vocoder_config = FastSpeech2ConformerHifiGanConfig(**config_kwargs)
+
+    vocoder = FastSpeech2ConformerHifiGan(vocoder_config)
+    load_weights(espnet_checkpoint, vocoder, vocoder_config)
+
+    # Prepare the model + vocoder
+    config = FastSpeech2ConformerWithHifiGanConfig.from_sub_model_configs(model_config, vocoder_config)
+    with_hifigan_model = FastSpeech2ConformerWithHifiGan(config)
+    with_hifigan_model.model = model
+    with_hifigan_model.vocoder = vocoder
+
+    with_hifigan_model.save_pretrained(pytorch_dump_folder_path)
+
+    if repo_id:
+        print("Pushing to the hub...")
+        with_hifigan_model.push_to_hub(repo_id)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
+    parser.add_argument(
+        "--yaml_config_path", required=True, default=None, type=str, help="Path to config.yaml of model to convert"
+    )
+    parser.add_argument(
+        "--pytorch_dump_folder_path",
+        required=True,
+        default=None,
+        type=str,
+        help="Path to the output `FastSpeech2ConformerModel` PyTorch model.",
+    )
+    parser.add_argument(
+        "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
+    )
+
+    args = parser.parse_args()
+
+    convert_FastSpeech2ConformerWithHifiGan_checkpoint(
+        args.checkpoint_path,
+        args.yaml_config_path,
+        args.pytorch_dump_folder_path,
+        args.push_to_hub,
+    )

src/transformers/models/fastspeech2_conformer/tokenization_fastspeech2_conformer.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Team and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+"""Tokenization classes for FastSpeech2Conformer."""
+import json
+import os
+from typing import Optional, Tuple
+
+import regex
+
+from ...tokenization_utils import PreTrainedTokenizer
+from ...utils import logging, requires_backends
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "vocab.json"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "espnet/fastspeech2_conformer": "https://huggingface.co/espnet/fastspeech2_conformer/raw/main/vocab.json",
+    },
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    # Set to somewhat arbitrary large number as the model input
+    # isn't constrained by the relative positional encoding
+    "espnet/fastspeech2_conformer": 4096,
+}
+
+
+class FastSpeech2ConformerTokenizer(PreTrainedTokenizer):
+    """
+    Construct a FastSpeech2Conformer tokenizer.
+
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        bos_token (`str`, *optional*, defaults to `"<sos/eos>"`):
+            The begin of sequence token. Note that for FastSpeech2, it is the same as the `eos_token`.
+        eos_token (`str`, *optional*, defaults to `"<sos/eos>"`):
+            The end of sequence token. Note that for FastSpeech2, it is the same as the `bos_token`.
+        pad_token (`str`, *optional*, defaults to `"<blank>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        should_strip_spaces (`bool`, *optional*, defaults to `False`):
+            Whether or not to strip the spaces from the list of tokens.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    model_input_names = ["input_ids", "attention_mask"]
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+
+    def __init__(
+        self,
+        vocab_file,
+        bos_token="<sos/eos>",
+        eos_token="<sos/eos>",
+        pad_token="<blank>",
+        unk_token="<unk>",
+        should_strip_spaces=False,
+        **kwargs,
+    ):
+        requires_backends(self, "g2p_en")
+
+        with open(vocab_file, encoding="utf-8") as vocab_handle:
+            self.encoder = json.load(vocab_handle)
+
+        import g2p_en
+
+        self.g2p = g2p_en.G2p()
+
+        self.decoder = {v: k for k, v in self.encoder.items()}
+
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            should_strip_spaces=should_strip_spaces,
+            **kwargs,
+        )
+
+        self.should_strip_spaces = should_strip_spaces
+
+    @property
+    def vocab_size(self):
+        return len(self.decoder)
+
+    def get_vocab(self):
+        "Returns vocab as a dict"
+        return dict(self.encoder, **self.added_tokens_encoder)
+
+    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
+        # expand symbols
+        text = regex.sub(";", ",", text)
+        text = regex.sub(":", ",", text)
+        text = regex.sub("-", " ", text)
+        text = regex.sub("&", "and", text)
+
+        # strip unnecessary symbols
+        text = regex.sub(r"[\(\)\[\]\<\>\"]+", "", text)
+
+        # strip whitespaces
+        text = regex.sub(r"\s+", " ", text)
+
+        text = text.upper()
+
+        return text, kwargs
+
+    def _tokenize(self, text):
+        """Returns a tokenized string."""
+        # phonemize
+        tokens = self.g2p(text)
+
+        if self.should_strip_spaces:
+            tokens = list(filter(lambda s: s != " ", tokens))
+
+        tokens.append(self.eos_token)
+
+        return tokens
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        return self.encoder.get(token, self.encoder.get(self.unk_token))
+
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.decoder.get(index, self.unk_token)
+
+    # Override since phonemes cannot be converted back to strings
+    def decode(self, token_ids, **kwargs):
+        logger.warn(
+            "Phonemes cannot be reliably converted to a string due to the one-many mapping, converting to tokens instead."
+        )
+        return self.convert_ids_to_tokens(token_ids)
+
+    # Override since phonemes cannot be converted back to strings
+    def convert_tokens_to_string(self, tokens, **kwargs):
+        logger.warn(
+            "Phonemes cannot be reliably converted to a string due to the one-many mapping, returning the tokens."
+        )
+        return tokens
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return
+        vocab_file = os.path.join(
+            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+        )
+
+        with open(vocab_file, "w", encoding="utf-8") as f:
+            f.write(json.dumps(self.get_vocab(), ensure_ascii=False))
+
+        return (vocab_file,)
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["g2p"] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+
+        try:
+            import g2p_en
+
+            self.g2p = g2p_en.G2p()
+        except ImportError:
+            raise ImportError(
+                "You need to install g2p-en to use FastSpeech2ConformerTokenizer. "
+                "See https://pypi.org/project/g2p-en/ for installation."
+            )

src/transformers/models/speecht5/modeling_speecht5.py

@@ -2655,6 +2655,7 @@ class SpeechT5ForTextToSpeech(SpeechT5PreTrainedModel):
         return_dict: Optional[bool] = None,
         speaker_embeddings: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.FloatTensor] = None,
+        stop_labels: Optional[torch.Tensor] = None,
     ) -> Union[Tuple, Seq2SeqSpectrogramOutput]:
         r"""
         input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
@@ -2973,6 +2974,7 @@ class SpeechT5ForSpeechToSpeech(SpeechT5PreTrainedModel):
         return_dict: Optional[bool] = None,
         speaker_embeddings: Optional[torch.FloatTensor] = None,
         labels: Optional[torch.FloatTensor] = None,
+        stop_labels: Optional[torch.Tensor] = None,
     ) -> Union[Tuple, Seq2SeqSpectrogramOutput]:
         r"""
         input_values (`torch.FloatTensor` of shape `(batch_size, sequence_length)`):

src/transformers/testing_utils.py

@@ -67,6 +67,7 @@ from .utils import (
     is_flax_available,
     is_fsdp_available,
     is_ftfy_available,
+    is_g2p_en_available,
     is_ipex_available,
     is_jieba_available,
     is_jinja_available,
@@ -365,6 +366,13 @@ def require_fsdp(test_case, min_version: str = "1.12.0"):
     )
 
 
+def require_g2p_en(test_case):
+    """
+    Decorator marking a test that requires g2p_en. These tests are skipped when SentencePiece isn't installed.
+    """
+    return unittest.skipUnless(is_g2p_en_available(), "test requires g2p_en")(test_case)
+
+
 def require_safetensors(test_case):
     """
     Decorator marking a test that requires safetensors. These tests are skipped when safetensors isn't installed.

src/transformers/utils/__init__.py

@@ -123,6 +123,7 @@ from .import_utils import (
     is_flax_available,
     is_fsdp_available,
     is_ftfy_available,
+    is_g2p_en_available,
     is_in_notebook,
     is_ipex_available,
     is_jieba_available,

src/transformers/utils/dummy_pt_objects.py

@@ -3422,6 +3422,37 @@ class FalconPreTrainedModel(metaclass=DummyObject):
         requires_backends(self, ["torch"])
 
 
+FASTSPEECH2_CONFORMER_PRETRAINED_MODEL_ARCHIVE_LIST = None
+
+
+class FastSpeech2ConformerHifiGan(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class FastSpeech2ConformerModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class FastSpeech2ConformerPreTrainedModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
+class FastSpeech2ConformerWithHifiGan(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 

src/transformers/utils/import_utils.py

@@ -94,6 +94,7 @@ except importlib.metadata.PackageNotFoundError:
     except importlib.metadata.PackageNotFoundError:
         _faiss_available = False
 _ftfy_available = _is_package_available("ftfy")
+_g2p_en_available = _is_package_available("g2p_en")
 _ipex_available, _ipex_version = _is_package_available("intel_extension_for_pytorch", return_version=True)
 _jieba_available = _is_package_available("jieba")
 _jinja_available = _is_package_available("jinja2")
@@ -444,6 +445,10 @@ def is_ftfy_available():
     return _ftfy_available
 
 
+def is_g2p_en_available():
+    return _g2p_en_available
+
+
 @lru_cache()
 def is_torch_tpu_available(check_device=True):
     "Checks if `torch_xla` is installed and potentially if a TPU is in the environment"
@@ -1059,6 +1064,12 @@ LEVENSHTEIN_IMPORT_ERROR = """
 install python-Levenshtein`. Please note that you may need to restart your runtime after installation.
 """
 
+# docstyle-ignore
+G2P_EN_IMPORT_ERROR = """
+{0} requires the g2p-en library but it was not found in your environment. You can install it with pip:
+`pip install g2p-en`. Please note that you may need to restart your runtime after installation.
+"""
+
 # docstyle-ignore
 PYTORCH_QUANTIZATION_IMPORT_ERROR = """
 {0} requires the pytorch-quantization library but it was not found in your environment. You can install it with pip:
@@ -1101,7 +1112,6 @@ SACREMOSES_IMPORT_ERROR = """
 `pip install sacremoses`. Please note that you may need to restart your runtime after installation.
 """
 
-
 # docstyle-ignore
 SCIPY_IMPORT_ERROR = """
 {0} requires the scipy library but it was not found in your environment. You can install it with pip:
@@ -1225,6 +1235,7 @@ BACKENDS_MAPPING = OrderedDict(
         ("faiss", (is_faiss_available, FAISS_IMPORT_ERROR)),
         ("flax", (is_flax_available, FLAX_IMPORT_ERROR)),
         ("ftfy", (is_ftfy_available, FTFY_IMPORT_ERROR)),
+        ("g2p_en", (is_g2p_en_available, G2P_EN_IMPORT_ERROR)),
         ("pandas", (is_pandas_available, PANDAS_IMPORT_ERROR)),
         ("phonemizer", (is_phonemizer_available, PHONEMIZER_IMPORT_ERROR)),
         ("pretty_midi", (is_pretty_midi_available, PRETTY_MIDI_IMPORT_ERROR)),

tests/models/fastspeech2_conformer/test_tokenization_fastspeech2_conformer.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+"""Tests for the FastSpeech2Conformer tokenizer."""
+
+import unittest
+
+from transformers.models.fastspeech2_conformer import FastSpeech2ConformerTokenizer
+from transformers.testing_utils import require_g2p_en, slow
+
+from ...test_tokenization_common import TokenizerTesterMixin
+
+
+@require_g2p_en
+class FastSpeech2ConformerTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
+    tokenizer_class = FastSpeech2ConformerTokenizer
+    test_rust_tokenizer = False
+
+    def setUp(self):
+        super().setUp()
+        tokenizer = FastSpeech2ConformerTokenizer.from_pretrained("espnet/fastspeech2_conformer")
+        tokenizer.save_pretrained(self.tmpdirname)
+
+    def get_input_output_texts(self, tokenizer):
+        input_text = "this is a test"
+        output_text = "this is a test"
+        return input_text, output_text
+
+    # Custom `get_clean_sequence` since FastSpeech2ConformerTokenizer can't decode id -> string
+    def get_clean_sequence(self, tokenizer, with_prefix_space=False, **kwargs):  # max_length=20, min_length=5
+        input_text, output_text = self.get_input_output_texts(tokenizer)
+        ids = tokenizer.encode(output_text, add_special_tokens=False)
+        return output_text, ids
+
+    def test_convert_token_and_id(self):
+        """Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
+        token = "<unk>"
+        token_id = 1
+
+        self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
+        self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
+
+    def test_get_vocab(self):
+        vocab_keys = list(self.get_tokenizer().get_vocab().keys())
+
+        self.assertEqual(vocab_keys[0], "<blank>")
+        self.assertEqual(vocab_keys[1], "<unk>")
+        self.assertEqual(vocab_keys[-4], "UH0")
+        self.assertEqual(vocab_keys[-2], "..")
+        self.assertEqual(vocab_keys[-1], "<sos/eos>")
+        self.assertEqual(len(vocab_keys), 78)
+
+    def test_vocab_size(self):
+        self.assertEqual(self.get_tokenizer().vocab_size, 78)
+
+    @unittest.skip(
+        "FastSpeech2Conformer tokenizer does not support adding tokens as they can't be added to the g2p_en backend"
+    )
+    def test_added_token_are_matched_longest_first(self):
+        pass
+
+    @unittest.skip(
+        "FastSpeech2Conformer tokenizer does not support adding tokens as they can't be added to the g2p_en backend"
+    )
+    def test_added_tokens_do_lower_case(self):
+        pass
+
+    @unittest.skip(
+        "FastSpeech2Conformer tokenizer does not support adding tokens as they can't be added to the g2p_en backend"
+    )
+    def test_tokenize_special_tokens(self):
+        pass
+
+    def test_full_tokenizer(self):
+        tokenizer = self.get_tokenizer()
+
+        tokens = tokenizer.tokenize("This is a test")
+        ids = [9, 12, 6, 12, 11, 2, 4, 15, 6, 4, 77]
+        self.assertListEqual(tokens, ["DH", "IH1", "S", "IH1", "Z", "AH0", "T", "EH1", "S", "T", "<sos/eos>"])
+        self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), ids)
+        self.assertListEqual(tokenizer.convert_ids_to_tokens(ids), tokens)
+
+    @slow
+    def test_tokenizer_integration(self):
+        # Custom test since:
+        # 1) This tokenizer only decodes to tokens (phonemes cannot be converted to text with complete accuracy)
+        # 2) Uses a sequence without numbers since espnet has different, custom number conversion.
+        # This tokenizer can phonemize numbers, but where in espnet "32" is phonemized as "thirty two",
+        # here "32" is phonemized as "thirty-two" because we haven't implemented the custom number handling.
+
+        sequences = [
+            "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
+            "general-purpose architectures (BERT, GPT, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
+            "Language Understanding (NLU) and Natural Language Generation (NLG) with over thirty-two pretrained "
+            "models in one hundred plus languages and deep interoperability between Jax, PyTorch and TensorFlow.",
+            "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
+            "conditioning on both left and right context in all layers.",
+            "The quick brown fox jumps over the lazy dog.",
+        ]
+        tokenizer = FastSpeech2ConformerTokenizer.from_pretrained(
+            "espnet/fastspeech2_conformer", revision="07f9c4a2d6bbc69b277d87d2202ad1e35b05e113"
+        )
+        actual_encoding = tokenizer(sequences)
+
+        # fmt: off
+        expected_encoding = {
+            'input_ids': [
+                [4, 7, 60, 3, 6, 22, 30, 7, 14, 21, 11, 22, 30, 7, 14, 21, 8, 29, 3, 34, 3, 18, 11, 17, 12, 4, 21, 10, 4, 7, 60, 3, 6, 22, 30, 7, 14, 21, 11, 2, 3, 5, 17, 12, 4, 21, 10, 17, 7, 29, 4, 7, 31, 3, 5, 25, 38, 4, 17, 7, 2, 20, 32, 5, 11, 40, 15, 3, 21, 2, 8, 17, 38, 17, 2, 6, 24, 7, 10, 2, 4, 45, 10, 39, 21, 11, 25, 38, 4, 23, 37, 15, 4, 6, 23, 7, 2, 25, 38, 4, 2, 23, 11, 8, 15, 14, 11, 23, 5, 13, 6, 4, 12, 8, 4, 21, 25, 23, 11, 8, 15, 3, 39, 2, 8, 1, 22, 30, 7, 3, 18, 39, 21, 2, 8, 8, 18, 36, 37, 16, 2, 40, 62, 3, 5, 21, 6, 4, 18, 3, 5, 13, 36, 3, 8, 28, 2, 3, 5, 3, 18, 39, 21, 2, 8, 8, 18, 36, 37, 16, 2, 40, 40, 45, 3, 21, 31, 35, 2, 3, 15, 8, 36, 16, 12, 9, 34, 20, 21, 43, 38, 5, 29, 4, 28, 17, 7, 29, 4, 7, 31, 3, 5, 14, 24, 5, 2, 8, 11, 13, 3, 16, 19, 3, 26, 19, 3, 5, 7, 2, 5, 17, 8, 19, 6, 8, 18, 36, 37, 16, 2, 40, 2, 11, 2, 3, 5, 5, 27, 17, 49, 3, 4, 21, 2, 17, 21, 25, 12, 8, 2, 4, 29, 25, 13, 4, 16, 27, 3, 40, 18, 10, 6, 23, 17, 12, 4, 21, 10, 2, 3, 5, 4, 15, 3, 6, 21, 8, 46, 22, 33, 77],
+                [25, 38, 4, 12, 11, 5, 13, 11, 32, 3, 5, 4, 28, 17, 7, 27, 4, 7, 31, 3, 5, 27, 17, 25, 51, 5, 13, 7, 15, 10, 35, 2, 3, 2, 8, 7, 45, 17, 7, 2, 11, 2, 3, 4, 31, 35, 2, 3, 11, 22, 7, 19, 14, 2, 3, 8, 31, 25, 2, 8, 5, 4, 15, 10, 6, 4, 25, 32, 40, 55, 3, 4, 8, 29, 10, 2, 3, 5, 12, 35, 2, 3, 13, 36, 24, 3, 25, 34, 43, 8, 15, 22, 4, 2, 3, 5, 7, 32, 4, 10, 24, 3, 4, 54, 10, 6, 4, 13, 3, 30, 8, 8, 31, 21, 11, 33, 77],
+                [9, 2, 10, 16, 12, 10, 25, 7, 42, 3, 22, 24, 10, 6, 40, 19, 14, 17, 6, 34, 20, 21, 9, 2, 8, 31, 11, 29, 5, 30, 37, 33, 77]
+            ],
+            'attention_mask': [
+                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
+                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
+            ]
+        }
+        # fmt: on
+
+        actual_tokens = [tokenizer.decode(input_ids) for input_ids in expected_encoding["input_ids"]]
+        expected_tokens = [
+            [tokenizer.convert_ids_to_tokens(id) for id in sequence] for sequence in expected_encoding["input_ids"]
+        ]
+
+        self.assertListEqual(actual_encoding["input_ids"], expected_encoding["input_ids"])
+        self.assertListEqual(actual_encoding["attention_mask"], expected_encoding["attention_mask"])
+        self.assertTrue(actual_tokens == expected_tokens)
+
+    @unittest.skip(
+        reason="FastSpeech2Conformer tokenizer does not support adding tokens as they can't be added to the g2p_en backend"
+    )
+    def test_add_tokens_tokenizer(self):
+        pass
+
+    @unittest.skip(
+        reason="FastSpeech2Conformer tokenizer does not support adding tokens as they can't be added to the g2p_en backend"
+    )
+    def test_add_special_tokens(self):
+        pass
+
+    @unittest.skip(
+        reason="FastSpeech2Conformer tokenizer does not support adding tokens as they can't be added to the g2p_en backend"
+    )
+    def test_added_token_serializable(self):
+        pass
+
+    @unittest.skip(
+        reason="FastSpeech2Conformer tokenizer does not support adding tokens as they can't be added to the g2p_en backend"
+    )
+    def test_save_and_load_tokenizer(self):
+        pass
+
+    @unittest.skip(reason="Phonemes cannot be reliably converted to string due to one-many mapping")
+    def test_internal_consistency(self):
+        pass
+
+    @unittest.skip(reason="Phonemes cannot be reliably converted to string due to one-many mapping")
+    def test_encode_decode_with_spaces(self):
+        pass
+
+    @unittest.skip(reason="Phonemes cannot be reliably converted to string due to one-many mapping")
+    def test_convert_tokens_to_string_format(self):
+        pass
+
+    @unittest.skip("FastSpeech2Conformer tokenizer does not support pairs.")
+    def test_maximum_encoding_length_pair_input(self):
+        pass
+
+    @unittest.skip(
+        "FastSpeech2Conformer tokenizer appends eos_token to each string it's passed, including `is_split_into_words=True`."
+    )
+    def test_pretokenized_inputs(self):
+        pass
+
+    @unittest.skip(
+        reason="g2p_en is slow is with large inputs and max encoding length is not a concern for FastSpeech2Conformer"
+    )
+    def test_maximum_encoding_length_single_input(self):
+        pass

utils/check_config_attributes.py

@@ -123,6 +123,7 @@ SPECIAL_CASES_TO_ALLOW.update(
         "DinatConfig": True,
         "DonutSwinConfig": True,
         "EfficientFormerConfig": True,
+        "FastSpeech2ConformerConfig": True,
         "FSMTConfig": True,
         "JukeboxConfig": True,
         "LayoutLMv2Config": True,

utils/check_repo.py

@@ -90,6 +90,8 @@ IGNORE_NON_TESTED = PRIVATE_MODELS.copy() + [
     "UMT5EncoderModel",  # Building part of bigger (tested) model.
     "Blip2QFormerModel",  # Building part of bigger (tested) model.
     "ErnieMForInformationExtraction",
+    "FastSpeech2ConformerHifiGan",  # Already tested by SpeechT5HifiGan (# Copied from)
+    "FastSpeech2ConformerWithHifiGan",  # Built with two smaller (tested) models.
     "GraphormerDecoderHead",  # Building part of bigger (tested) model.
     "JukeboxVQVAE",  # Building part of bigger (tested) model.
     "JukeboxPrior",  # Building part of bigger (tested) model.
@@ -159,6 +161,8 @@ IGNORE_NON_AUTO_CONFIGURED = PRIVATE_MODELS.copy() + [
     "Blip2QFormerModel",
     "Blip2VisionModel",
     "ErnieMForInformationExtraction",
+    "FastSpeech2ConformerHifiGan",
+    "FastSpeech2ConformerWithHifiGan",
     "GitVisionModel",
     "GraphormerModel",
     "GraphormerForGraphClassification",