TTS fine-tuning for SpeechT5 (#21824)

* wrong argument name

* append eos_token_id

* all tokenizers need mask and ctc_blank tokens

* remove reduction factor from feature extractor

* add proper TTS loss

* did shifting the wrong way around

* mask out padded portions

* remove logits again (don't really need it)

* fix unit tests

* fixup

* pad also returns the decoder attention mask, since that's useful to have

* clean up feature extractor logic

* pad can handle TTS task too

* remove stop_labels from loss calculation

* simplify logic

* fixup

* do -100 masking properly

* small STFT optimization (calculate mel filterbanks only once)

* replace torchaudio fbanks with audio_utils

* remove torchaudio dependency

* simplify & speed up the STFT

* don't serialize window and mel filters

* output cross attentions when generating speech

* add guided attention loss

* fix failing test

* Update src/transformers/models/speecht5/feature_extraction_speecht5.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* Update src/transformers/models/speecht5/modeling_speecht5.py
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>

* change type annotation of attention_mask to LongTensor

* extract loss into class

* remove unused frame_signal_scale argument

* use config object in loss class

* fix type annotations in doc comments

* change optional to just bool

* implement missing tokenizer method

* add deprecation warning

* Update src/transformers/models/speecht5/feature_extraction_speecht5.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* Update src/transformers/models/speecht5/feature_extraction_speecht5.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* add deprecation warning for stop_labels

---------
Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/models/speecht5/configuration_speecht5.py

@@ -161,13 +161,22 @@ class SpeechT5Config(PretrainedConfig):
         speech_decoder_postnet_dropout (`float`, *optional*, defaults to 0.5):
             The dropout probability for the speech decoder post-net layers.
         reduction_factor (`int`, *optional*, defaults to 2):
-            Spectrogram length reduction factor for the speech decoder post-net.
+            Spectrogram length reduction factor for the speech decoder inputs.
         max_speech_positions (`int`, *optional*, defaults to 4000):
             The maximum sequence length of speech features that this model might ever be used with.
         max_text_positions (`int`, *optional*, defaults to 450):
             The maximum sequence length of text features that this model might ever be used with.
         encoder_max_relative_position (`int`, *optional*, defaults to 160):
             Maximum distance for relative position embedding in the encoder.
+        use_guided_attention_loss (`bool`, *optional*, defaults to `True`):
+            Whether to apply guided attention loss while training the TTS model.
+        guided_attention_loss_num_heads (`int`, *optional*, defaults to 2):
+            Number of attention heads the guided attention loss will be applied to. Use -1 to apply this loss to all
+            attention heads.
+        guided_attention_loss_sigma (`float`, *optional*, defaults to 0.4):
+            Standard deviation for guided attention loss.
+        guided_attention_loss_scale (`float`, *optional*, defaults to 10.0):
+            Scaling coefficient for guided attention loss (also known as lambda).
         use_cache (`bool`, *optional*, defaults to `True`):
             Whether or not the model should return the last key/values attentions (not used by all models).
 
@@ -241,6 +250,10 @@ class SpeechT5Config(PretrainedConfig):
         max_speech_positions=4000,
         max_text_positions=450,
         encoder_max_relative_position=160,
+        use_guided_attention_loss=True,
+        guided_attention_loss_num_heads=2,
+        guided_attention_loss_sigma=0.4,
+        guided_attention_loss_scale=10.0,
         use_cache=True,
         is_encoder_decoder=True,
         **kwargs,
@@ -311,6 +324,12 @@ class SpeechT5Config(PretrainedConfig):
         self.max_speech_positions = max_speech_positions
         self.max_text_positions = max_text_positions
         self.encoder_max_relative_position = encoder_max_relative_position
+
+        self.use_guided_attention_loss = use_guided_attention_loss
+        self.guided_attention_loss_num_heads = guided_attention_loss_num_heads
+        self.guided_attention_loss_sigma = guided_attention_loss_sigma
+        self.guided_attention_loss_scale = guided_attention_loss_scale
+
         self.use_cache = use_cache
         self.is_encoder_decoder = is_encoder_decoder
 

src/transformers/models/speecht5/convert_speecht5_original_pytorch_checkpoint_to_pytorch.py

@@ -351,12 +351,11 @@ def convert_speecht5_checkpoint(
     if vocab_path:
         tokenizer = SpeechT5Tokenizer(vocab_path, model_max_length=config.max_text_positions)
 
-        if task == "pretrain":
-            # Mask token behaves like a normal word, i.e. include the space before it
-            mask_token = AddedToken("<mask>", lstrip=True, rstrip=False)
-            tokenizer.mask_token = mask_token
-            tokenizer.add_special_tokens({"mask_token": mask_token})
-            tokenizer.add_tokens(["<ctc_blank>"])
+        # Mask token behaves like a normal word, i.e. include the space before it
+        mask_token = AddedToken("<mask>", lstrip=True, rstrip=False)
+        tokenizer.mask_token = mask_token
+        tokenizer.add_special_tokens({"mask_token": mask_token})
+        tokenizer.add_tokens(["<ctc_blank>"])
 
     feature_extractor = SpeechT5FeatureExtractor()
     processor = SpeechT5Processor(tokenizer=tokenizer, feature_extractor=feature_extractor)

src/transformers/models/speecht5/feature_extraction_speecht5.py

@@ -14,12 +14,13 @@
 # limitations under the License.
 """Feature extractor class for SpeechT5."""
 
-from typing import List, Optional, Union
+import warnings
+from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
 import torch
-import torchaudio
 
+from ...audio_utils import get_mel_filter_banks
 from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
 from ...feature_extraction_utils import BatchFeature
 from ...utils import PaddingStrategy, TensorType, logging
@@ -60,7 +61,7 @@ class SpeechT5FeatureExtractor(SequenceFeatureExtractor):
         win_function (`str`, *optional*, defaults to `"hann_window"`):
             Name for the window function used for windowing, must be accessible via `torch.{win_function}`
         frame_signal_scale (`float`, *optional*, defaults to 1.0):
-            Constant multiplied in creating the frames before applying DFT.
+            Constant multiplied in creating the frames before applying DFT. This argument is deprecated.
         fmin (`float`, *optional*, defaults to 80):
             Minimum mel frequency in Hz.
         fmax (`float`, *optional*, defaults to 7600):
@@ -68,7 +69,7 @@ class SpeechT5FeatureExtractor(SequenceFeatureExtractor):
         mel_floor (`float`, *optional*, defaults to 1e-10):
             Minimum value of mel frequency banks.
         reduction_factor (`int`, *optional*, defaults to 2):
-            Spectrogram length reduction factor.
+            Spectrogram length reduction factor. This argument is deprecated.
         return_attention_mask (`bool`, *optional*, defaults to `True`):
             Whether or not [`~SpeechT5FeatureExtractor.__call__`] should return `attention_mask`.
     """
@@ -109,10 +110,33 @@ class SpeechT5FeatureExtractor(SequenceFeatureExtractor):
 
         self.sample_size = win_length * sampling_rate // 1000
         self.sample_stride = hop_length * sampling_rate // 1000
-
         self.n_fft = 2 ** int(np.ceil(np.log2(self.sample_size)))
         self.n_freqs = (self.n_fft // 2) + 1
 
+        window = getattr(torch, self.win_function)(window_length=self.sample_size, periodic=True)
+        self.window = window.numpy().astype(np.float64)
+
+        self.mel_filters = get_mel_filter_banks(
+            nb_frequency_bins=self.n_freqs,
+            nb_mel_filters=self.num_mel_bins,
+            frequency_min=self.fmin,
+            frequency_max=self.fmax,
+            sample_rate=self.sampling_rate,
+            norm="slaney",
+            mel_scale="slaney",
+        )
+
+        if frame_signal_scale != 1.0:
+            warnings.warn(
+                "The argument `frame_signal_scale` is deprecated and will be removed in version 4.30.0 of Transformers",
+                FutureWarning,
+            )
+        if reduction_factor != 2.0:
+            warnings.warn(
+                "The argument `reduction_factor` is deprecated and will be removed in version 4.30.0 of Transformers",
+                FutureWarning,
+            )
+
     @staticmethod
     # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
     def zero_mean_unit_var_norm(
@@ -137,99 +161,45 @@ class SpeechT5FeatureExtractor(SequenceFeatureExtractor):
         return normed_input_values
 
     @staticmethod
-    def _center_pad(one_waveform, n_fft, pad_mode):
-        padding = [(int(n_fft // 2), int(n_fft // 2))]
-        return np.pad(one_waveform, padding, mode=pad_mode)
-
-    @staticmethod
-    # Copied from transformers.models.mctct.feature_extraction_mctct.MCTCTFeatureExtractor._num_frames_calc
-    def _num_frames_calc(in_size, frame_size, frame_stride):
-        return int(1 + np.floor((in_size - frame_size) * 1 / frame_stride))
-
-    @staticmethod
-    # Copied from transformers.models.mctct.feature_extraction_mctct.MCTCTFeatureExtractor._frame_signal
-    def _frame_signal(one_waveform, n_frames, frame_signal_scale, window_length, sample_stride):
-        scale = frame_signal_scale
-        frames = np.zeros(n_frames * window_length)
-        for frame_idx in range(n_frames):
-            start = frame_idx * window_length
-            end = (frame_idx + 1) * window_length
-            wave_start = frame_idx * sample_stride
-            wave_end = frame_idx * sample_stride + window_length
-            frames[start:end] = scale * one_waveform[wave_start:wave_end]
-
-        return frames
-
-    @staticmethod
-    # Copied from transformers.models.mctct.feature_extraction_mctct.MCTCTFeatureExtractor._windowing
-    def _windowing(frames, window_length, window):
-        if frames.size % window_length != 0:
-            raise ValueError(
-                f"`frames` is supposed to have length divisble by `window_length`, but is {frames.size} with"
-                f" window_length={window_length}."
-            )
-
-        shaped = frames.reshape(-1, window_length)
-        shaped = window * shaped
-        return shaped
-
-    @staticmethod
-    # Copied from transformers.models.mctct.feature_extraction_mctct.MCTCTFeatureExtractor._dft
-    def _dft(frames, K, n_frames, n_samples, n_fft):
-        dft = np.zeros([n_frames, K])
+    def _stft(waveform: np.ndarray, fft_length: int, hop_length: int, window: np.ndarray) -> np.ndarray:
+        """
+        Calculates the magnitude spectrogram over one waveform array.
+        """
+        # center pad the waveform
+        padding = [(int(fft_length // 2), int(fft_length // 2))]
+        waveform = np.pad(waveform, padding, mode="reflect")
+        waveform_size = waveform.size
 
-        for frame in range(n_frames):
-            begin = frame * n_samples
+        # promote to float64, since np.fft uses float64 internally
+        waveform = waveform.astype(np.float64)
 
-            inwards_buffer = frames[begin : begin + n_samples]
-            inwards_buffer = np.pad(inwards_buffer, (0, n_fft - n_samples), "constant")
-            out = np.fft.rfft(inwards_buffer)
+        num_frames = int(1 + np.floor((waveform_size - fft_length) / hop_length))
+        num_frequency_bins = (fft_length // 2) + 1
+        spectrogram = np.empty((num_frames, num_frequency_bins))
 
-            dft[frame] = np.abs(out[:K])
+        start = 0
+        for frame_idx in range(num_frames):
+            frame = waveform[start : start + fft_length] * window
+            spectrogram[frame_idx] = np.abs(np.fft.rfft(frame))
+            start += hop_length
 
-        return dft
+        return spectrogram
 
-    def _extract_fbank_features(
+    def _extract_mel_features(
         self,
         one_waveform: np.ndarray,
     ) -> np.ndarray:
         """
-        Extracts log-mel filterbank features for one waveform vector (unbatched). Adapted from Flashlight's C++ MFSC
-        code and librosa.
+        Extracts log-mel filterbank features for one waveform array (unbatched).
         """
-        one_waveform = self._center_pad(one_waveform, self.n_fft, "reflect")
-
-        n_frames = self._num_frames_calc(one_waveform.size, self.sample_size, self.sample_stride)
-
-        frames = self._frame_signal(
-            one_waveform, n_frames, self.frame_signal_scale, self.sample_size, self.sample_stride
-        )
-
-        window = getattr(torch, self.win_function)(window_length=self.sample_size, periodic=True)
-        window = window.numpy()
-
-        frames = self._windowing(frames, self.sample_size, window)
-
-        dft_out = self._dft(frames.flatten(), self.n_freqs, n_frames, self.sample_size, self.n_fft)
-
-        fbanks = torchaudio.functional.melscale_fbanks(
-            n_freqs=self.n_freqs,
-            f_min=self.fmin,
-            f_max=self.fmax,
-            n_mels=self.num_mel_bins,
-            sample_rate=self.sampling_rate,
-            norm="slaney",
-            mel_scale="slaney",
-        )
-        fbanks = fbanks.numpy()
+        if self.n_fft != self.sample_size:
+            raise NotImplementedError(
+                f"Currently the STFT frame size must be a power of two, but got {self.sample_size} for a window length of {self.win_length} and sampling rate of {self.sampling_rate}. Ensure `win_length * sampling_rate // 1000` is divisible by two."
+            )
 
-        return np.log10(np.maximum(self.mel_floor, np.dot(dft_out, fbanks)))
+        stft_out = self._stft(one_waveform, self.n_fft, self.sample_stride, self.window)
 
-    def _reduce(self, inputs):
-        reduced = []
-        for i in range(len(inputs)):
-            reduced.append(inputs[i][self.reduction_factor - 1 :: self.reduction_factor])
-        return reduced
+        return np.log10(np.maximum(self.mel_floor, np.dot(stft_out, self.mel_filters)))
 
     def __call__(
         self,
@@ -341,7 +311,6 @@ class SpeechT5FeatureExtractor(SequenceFeatureExtractor):
                 return inputs_target
             else:
                 inputs["labels"] = inputs_target["input_values"]
-                inputs["stop_labels"] = inputs_target["stop_labels"]
                 decoder_attention_mask = inputs_target.get("attention_mask")
                 if decoder_attention_mask is not None:
                     inputs["decoder_attention_mask"] = decoder_attention_mask
@@ -381,8 +350,7 @@ class SpeechT5FeatureExtractor(SequenceFeatureExtractor):
 
         # convert into correct format for padding
         if is_target:
-            features = [self._extract_fbank_features(waveform) for waveform in speech]
-            fbank_sizes = [len(x) for x in features]
+            features = [self._extract_mel_features(waveform) for waveform in speech]
             encoded_inputs = BatchFeature({"input_values": features})
             self.feature_size = self.num_mel_bins
         else:
@@ -429,22 +397,18 @@ class SpeechT5FeatureExtractor(SequenceFeatureExtractor):
                 padded_inputs["input_values"], attention_mask=attention_mask, padding_value=self.padding_value
             )
 
-        if is_target:
-            # make labels for stop prediction
-            stop_labels = []
-            for i, l in enumerate(fbank_sizes):
-                labels = np.zeros(len(padded_inputs["input_values"][i]))
-                labels[l - 1 :] = 1.0
-                stop_labels.append(labels)
-            padded_inputs["stop_labels"] = stop_labels
-
-            # thin out frames for reduction factor
-            if self.reduction_factor > 1:
-                padded_inputs["input_values"] = self._reduce(padded_inputs["input_values"])
-                if attention_mask is not None:
-                    padded_inputs["attention_mask"] = self._reduce(padded_inputs["attention_mask"])
-
         if return_tensors is not None:
             padded_inputs = padded_inputs.convert_to_tensors(return_tensors)
 
         return padded_inputs
+
+    def to_dict(self) -> Dict[str, Any]:
+        output = super().to_dict()
+
+        # Don't serialize these as they are derived from the other properties.
+        names = ["window", "mel_filters", "sample_size", "sample_stride", "n_fft", "n_freqs"]
+        for name in names:
+            if name in output:
+                del output[name]
+
+        return output

src/transformers/models/speecht5/processing_speecht5.py

@@ -87,59 +87,85 @@ class SpeechT5Processor(ProcessorMixin):
             inputs = None
 
         if audio_target is not None:
-            audio_target_features = self.feature_extractor(
-                audio_target=audio_target, *args, sampling_rate=sampling_rate, **kwargs
-            )
-            if inputs is None:
-                return audio_target_features
-            else:
-                inputs["labels"] = audio_target_features["input_values"]
-                inputs["stop_labels"] = audio_target_features["stop_labels"]
-                decoder_attention_mask = audio_target_features.get("attention_mask")
-                if decoder_attention_mask is not None:
-                    inputs["decoder_attention_mask"] = decoder_attention_mask
-
-        if text_target is not None:
-            encodings_target = self.tokenizer(text_target, **kwargs)
-            if inputs is None:
-                return encodings_target
-            else:
-                inputs["labels"] = encodings_target["input_ids"]
-                decoder_attention_mask = encodings_target.get("attention_mask")
-                if decoder_attention_mask is not None:
-                    inputs["decoder_attention_mask"] = decoder_attention_mask
+            targets = self.feature_extractor(audio_target=audio_target, *args, sampling_rate=sampling_rate, **kwargs)
+            labels = targets["input_values"]
+        elif text_target is not None:
+            targets = self.tokenizer(text_target, **kwargs)
+            labels = targets["input_ids"]
+        else:
+            targets = None
+
+        if inputs is None:
+            return targets
+
+        if targets is not None:
+            inputs["labels"] = labels
+
+            decoder_attention_mask = targets.get("attention_mask")
+            if decoder_attention_mask is not None:
+                inputs["decoder_attention_mask"] = decoder_attention_mask
 
         return inputs
 
     def pad(self, *args, **kwargs):
         """
-        This method forwards all its arguments to SpeechT5FeatureExtractor's [`~SpeechT5FeatureExtractor.pad`] and
-        returns its output.
+        Collates the audio and text inputs, as well as their targets, into a padded batch.
+
+        Audio inputs are padded by SpeechT5FeatureExtractor's [`~SpeechT5FeatureExtractor.pad`]. Text inputs are padded
+        by SpeechT5Tokenizer's [`~SpeechT5Tokenizer.pad`].
 
-        You can process your labels by using the argument `text` (either in the same call as your audio inputs, or in a
-        separate call). This forwards its arguments to SpeechT5Tokenizer's [`~SpeechT5Tokenizer.pad`].
+        Valid input combinations are:
+
+        - `input_ids` only
+        - `input_values` only
+        - `labels` only, either log-mel spectrograms or text tokens
+        - `input_ids` and log-mel spectrogram `labels`
+        - `input_values` and text `labels`
 
         Please refer to the docstring of the above two methods for more information.
         """
-        input_features = kwargs.pop("input_features", None)
+        input_values = kwargs.pop("input_values", None)
+        input_ids = kwargs.pop("input_ids", None)
         labels = kwargs.pop("labels", None)
 
-        if len(args) > 0:
-            input_features = args[0]
-            args = args[1:]
+        if input_values is not None and input_ids is not None:
+            raise ValueError("Cannot process both `input_values` and `input_ids` inputs.")
+        if input_values is None and input_ids is None and labels is None:
+            raise ValueError(
+                "You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded."
+            )
+
+        if input_values is not None:
+            inputs = self.feature_extractor.pad(input_values, *args, **kwargs)
+        elif input_ids is not None:
+            inputs = self.tokenizer.pad(input_ids, **kwargs)
+        else:
+            inputs = None
 
-        if input_features is not None:
-            input_features = self.feature_extractor.pad(input_features, *args, **kwargs)
         if labels is not None:
-            labels = self.tokenizer.pad(labels, **kwargs)
-
-        if labels is None:
-            return input_features
-        elif input_features is None:
-            return labels
+            if "input_ids" in labels or (isinstance(labels, list) and "input_ids" in labels[0]):
+                targets = self.tokenizer.pad(labels, **kwargs)
+                labels = targets["input_ids"]
+            else:
+                feature_size_hack = self.feature_extractor.feature_size
+                self.feature_extractor.feature_size = self.feature_extractor.num_mel_bins
+                targets = self.feature_extractor.pad(labels, *args, **kwargs)
+                self.feature_extractor.feature_size = feature_size_hack
+                labels = targets["input_values"]
         else:
-            input_features["labels"] = labels["input_ids"]
-            return input_features
+            targets = None
+
+        if inputs is None:
+            return targets
+
+        if targets is not None:
+            inputs["labels"] = labels
+
+            decoder_attention_mask = targets.get("attention_mask")
+            if decoder_attention_mask is not None:
+                inputs["decoder_attention_mask"] = decoder_attention_mask
+
+        return inputs
 
     def batch_decode(self, *args, **kwargs):
         """

src/transformers/models/speecht5/tokenization_speecht5.py

@@ -167,6 +167,26 @@ class SpeechT5Tokenizer(PreTrainedTokenizer):
         out_string += self.sp_model.decode(current_sub_tokens)
         return out_string.strip()
 
+    def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None) -> List[int]:
+        """Build model inputs from a sequence by appending eos_token_id."""
+        if token_ids_1 is None:
+            return token_ids_0 + [self.eos_token_id]
+        # We don't expect to process pairs, but leave the pair logic for API consistency
+        return token_ids_0 + token_ids_1 + [self.eos_token_id]
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        suffix_ones = [1]
+        if token_ids_1 is None:
+            return ([0] * len(token_ids_0)) + suffix_ones
+        return ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
+
     def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
         if not os.path.isdir(save_directory):
             logger.error(f"Vocabulary path ({save_directory}) should be a directory")

tests/models/speecht5/test_feature_extraction_speecht5.py

@@ -21,7 +21,7 @@ import unittest
 import numpy as np
 
 from transformers import BatchFeature, is_speech_available
-from transformers.testing_utils import require_torch, require_torchaudio
+from transformers.testing_utils import require_torch
 from transformers.utils.import_utils import is_torch_available
 
 from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
@@ -67,11 +67,9 @@ class SpeechT5FeatureExtractionTester(unittest.TestCase):
         hop_length=16,
         win_length=64,
         win_function="hann_window",
-        frame_signal_scale=1.0,
         fmin=80,
         fmax=7600,
         mel_floor=1e-10,
-        reduction_factor=2,
         return_attention_mask=True,
     ):
         self.parent = parent
@@ -87,11 +85,9 @@ class SpeechT5FeatureExtractionTester(unittest.TestCase):
         self.hop_length = hop_length
         self.win_length = win_length
         self.win_function = win_function
-        self.frame_signal_scale = frame_signal_scale
         self.fmin = fmin
         self.fmax = fmax
         self.mel_floor = mel_floor
-        self.reduction_factor = reduction_factor
         self.return_attention_mask = return_attention_mask
 
     def prepare_feat_extract_dict(self):
@@ -104,11 +100,9 @@ class SpeechT5FeatureExtractionTester(unittest.TestCase):
             "hop_length": self.hop_length,
             "win_length": self.win_length,
             "win_function": self.win_function,
-            "frame_signal_scale": self.frame_signal_scale,
             "fmin": self.fmin,
             "fmax": self.fmax,
             "mel_floor": self.mel_floor,
-            "reduction_factor": self.reduction_factor,
             "return_attention_mask": self.return_attention_mask,
         }
 
@@ -147,7 +141,6 @@ class SpeechT5FeatureExtractionTester(unittest.TestCase):
 
 
 @require_torch
-@require_torchaudio
 class SpeechT5FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.TestCase):
     feature_extraction_class = SpeechT5FeatureExtractor if is_speech_available() else None
 
@@ -407,10 +400,10 @@ class SpeechT5FeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest
     def test_integration_target(self):
         # fmt: off
         EXPECTED_INPUT_VALUES = torch.tensor(
-            [-2.7713, -2.8896, -3.2619, -3.0843, -2.9919, -3.0084, -3.2796, -3.3169,
-             -3.2397, -3.2053, -2.9151, -2.7921, -2.9403, -2.7411, -3.0654, -2.8314,
-             -3.0026, -2.9797, -3.1314, -2.9939, -2.6748, -2.7725, -2.8563, -2.9462,
-             -3.2623, -3.3044, -3.1318, -3.2672, -3.4030, -3.1988]
+            [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
+             -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
+             -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
+             -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]
         )
         # fmt: on
 

tests/models/speecht5/test_modeling_speecht5.py

@@ -25,10 +25,10 @@ from transformers.testing_utils import (
     require_sentencepiece,
     require_tokenizers,
     require_torch,
-    require_torchaudio,
     slow,
     torch_device,
 )
+from transformers.trainer_utils import set_seed
 from transformers.utils import cached_property
 
 from ...test_configuration_common import ConfigTester
@@ -716,7 +716,6 @@ class SpeechT5ForSpeechToTextTest(ModelTesterMixin, unittest.TestCase):
 
 
 @require_torch
-@require_torchaudio
 @require_sentencepiece
 @require_tokenizers
 @slow
@@ -991,7 +990,6 @@ class SpeechT5ForTextToSpeechTest(ModelTesterMixin, unittest.TestCase):
 
 
 @require_torch
-@require_torchaudio
 @require_sentencepiece
 @require_tokenizers
 @slow
@@ -1005,11 +1003,13 @@ class SpeechT5ForTextToSpeechIntegrationTests(unittest.TestCase):
         model.to(torch_device)
         processor = self.default_processor
 
+        set_seed(555)  # make deterministic
+
         input_text = "mister quilter is the apostle of the middle classes and we are glad to welcome his gospel"
         input_ids = processor(text=input_text, return_tensors="pt").input_ids.to(torch_device)
 
         generated_speech = model.generate_speech(input_ids)
-        self.assertEqual(generated_speech.shape, (1800, model.config.num_mel_bins))
+        self.assertEqual(generated_speech.shape, (1820, model.config.num_mel_bins))
 
 
 @require_torch
@@ -1406,7 +1406,6 @@ class SpeechT5ForSpeechToSpeechTest(ModelTesterMixin, unittest.TestCase):
 
 
 @require_torch
-@require_torchaudio
 @require_sentencepiece
 @require_tokenizers
 @slow

tests/models/speecht5/test_processor_speecht5.py

@@ -21,7 +21,7 @@ import unittest
 
 from transformers import is_speech_available, is_torch_available
 from transformers.models.speecht5 import SpeechT5Tokenizer
-from transformers.testing_utils import get_tests_dir, require_torch, require_torchaudio
+from transformers.testing_utils import get_tests_dir, require_torch
 from transformers.utils import FEATURE_EXTRACTOR_NAME
 
 
@@ -35,7 +35,6 @@ SAMPLE_VOCAB = get_tests_dir("fixtures/test_sentencepiece_bpe_char.model")
 
 
 @require_torch
-@require_torchaudio
 class SpeechT5ProcessorTest(unittest.TestCase):
     def setUp(self):
         self.tmpdirname = tempfile.mkdtemp()
@@ -52,7 +51,6 @@ class SpeechT5ProcessorTest(unittest.TestCase):
             "hop_length": 16,
             "win_length": 64,
             "win_function": "hann_window",
-            "frame_signal_scale": 1.0,
             "fmin": 80,
             "fmax": 7600,
             "mel_floor": 1e-10,

tests/models/speecht5/test_tokenization_speecht5.py

@@ -19,6 +19,7 @@ import unittest
 from transformers import SPIECE_UNDERLINE
 from transformers.models.speecht5 import SpeechT5Tokenizer
 from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
+from transformers.tokenization_utils import AddedToken
 
 from ...test_tokenization_common import TokenizerTesterMixin
 
@@ -38,6 +39,12 @@ class SpeechT5TokenizerTest(TokenizerTesterMixin, unittest.TestCase):
 
         # We have a SentencePiece fixture for testing
         tokenizer = SpeechT5Tokenizer(SAMPLE_VOCAB)
+
+        mask_token = AddedToken("<mask>", lstrip=True, rstrip=False)
+        tokenizer.mask_token = mask_token
+        tokenizer.add_special_tokens({"mask_token": mask_token})
+        tokenizer.add_tokens(["<ctc_blank>"])
+
         tokenizer.save_pretrained(self.tmpdirname)
 
     def get_input_output_texts(self, tokenizer):
@@ -64,8 +71,10 @@ class SpeechT5TokenizerTest(TokenizerTesterMixin, unittest.TestCase):
 
         self.assertEqual(vocab_keys[0], "<s>")
         self.assertEqual(vocab_keys[1], "<pad>")
-        self.assertEqual(vocab_keys[-2], "œ")
-        self.assertEqual(len(vocab_keys), 79)
+        self.assertEqual(vocab_keys[-4], "œ")
+        self.assertEqual(vocab_keys[-2], "<mask>")
+        self.assertEqual(vocab_keys[-1], "<ctc_blank>")
+        self.assertEqual(len(vocab_keys), 81)
 
     def test_vocab_size(self):
         self.assertEqual(self.get_tokenizer().vocab_size, 79)
@@ -175,7 +184,18 @@ class SpeechT5TokenizerTest(TokenizerTesterMixin, unittest.TestCase):
         ]
 
         # fmt: off
-        expected_encoding = {'input_ids': [[4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26], [4, 40, 47, 54, 32, 4, 10, 12, 4, 14, 5, 12, 10, 21, 9, 5, 14, 4, 6, 8, 4, 24, 13, 5, 39, 6, 13, 7, 10, 9, 4, 14, 5, 5, 24, 4, 25, 10, 14, 10, 13, 5, 17, 6, 10, 8, 9, 7, 15, 4, 13, 5, 24, 13, 5, 12, 5, 9, 6, 7, 6, 10, 8, 9, 12, 4, 19, 13, 8, 18, 4, 16, 9, 15, 7, 25, 5, 15, 5, 14, 4, 6, 5, 37, 6, 4, 25, 22, 4, 46, 8, 10, 9, 6, 15, 22, 4, 17, 8, 9, 14, 10, 6, 10, 8, 9, 10, 9, 21, 4, 8, 9, 4, 25, 8, 6, 11, 4, 15, 5, 19, 6, 4, 7, 9, 14, 4, 13, 10, 21, 11, 6, 4, 17, 8, 9, 6, 5, 37, 6, 4, 10, 9, 4, 7, 15, 15, 4, 15, 7, 22, 5, 13, 12, 26, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [4, 32, 11, 5, 4, 45, 16, 10, 17, 28, 4, 25, 13, 8, 20, 9, 4, 19, 8, 37, 4, 46, 16, 18, 24, 12, 4, 8, 27, 5, 13, 4, 6, 11, 5, 4, 15, 7, 57, 22, 4, 14, 8, 21, 26, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '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, 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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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+        expected_encoding = {
+            'input_ids': [
+                [4, 32, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 64, 19, 8, 13, 18, 5, 13, 15, 22, 4, 28, 9, 8, 20, 9, 4, 7, 12, 4, 24, 22, 6, 8, 13, 17, 11, 39, 6, 13, 7, 9, 12, 19, 8, 13, 18, 5, 13, 12, 4, 7, 9, 14, 4, 24, 22, 6, 8, 13, 17, 11, 39, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 39, 25, 5, 13, 6, 63, 4, 24, 13, 8, 27, 10, 14, 5, 12, 4, 21, 5, 9, 5, 13, 7, 15, 39, 24, 16, 13, 24, 8, 12, 5, 4, 7, 13, 17, 11, 10, 6, 5, 17, 6, 16, 13, 5, 12, 4, 64, 40, 47, 54, 32, 23, 4, 53, 49, 32, 23, 4, 54, 8, 40, 47, 54, 32, 7, 23, 4, 69, 52, 43, 23, 4, 51, 10, 12, 6, 10, 15, 40, 5, 13, 6, 23, 4, 69, 52, 48, 5, 6, 26, 26, 26, 63, 4, 19, 8, 13, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 61, 9, 14, 5, 13, 12, 6, 7, 9, 14, 10, 9, 21, 4, 64, 48, 52, 61, 63, 4, 7, 9, 14, 4, 48, 7, 6, 16, 13, 7, 15, 4, 52, 7, 9, 21, 16, 7, 21, 5, 4, 53, 5, 9, 5, 13, 7, 6, 10, 8, 9, 4, 64, 48, 52, 53, 63, 4, 20, 10, 6, 11, 4, 8, 27, 5, 13, 4, 6, 11, 10, 13, 6, 22, 39, 6, 20, 8, 4, 24, 13, 5, 6, 13, 7, 10, 9, 5, 14, 4, 18, 8, 14, 5, 15, 12, 4, 10, 9, 4, 8, 9, 5, 4, 11, 16, 9, 14, 13, 5, 14, 4, 24, 15, 16, 12, 4, 15, 7, 9, 21, 16, 7, 21, 5, 12, 4, 7, 9, 14, 4, 14, 5, 5, 24, 4, 10, 9, 6, 5, 13, 8, 24, 5, 13, 7, 25, 10, 15, 10, 6, 22, 4, 25, 5, 6, 20, 5, 5, 9, 4, 58, 7, 37, 23, 4, 49, 22, 32, 8, 13, 17, 11, 4, 7, 9, 14, 4, 32, 5, 9, 12, 8, 13, 55, 15, 8, 20, 26, 2],
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+            ],
+            'attention_mask': [
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+            ]
+        }
         # fmt: on
 
         self.tokenizer_integration_test_util(