Add Speech Seq2Seq Training script (#14792)

* start

* add gradient checkpointing and feature extractor freezing

* Apply suggestions from code review

* up

* up

* up

* correct

* up

* more changes

* up

* up

* up

* remove rst

examples/pytorch/speech-recognition/README.md

@@ -14,12 +14,27 @@ See the License for the specific language governing permissions and
 limitations under the License.
 -->
 
-# Automatic Speech Recognition examples
-
-
-## Connectionist Temporal Classification without Language Model (CTC w/o LM)
-
-The script [`run_speech_recognition_ctc.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) can be used to fine-tune any pretrained [Connectionist Temporal Classification Model](https://huggingface.co/transformers/master/model_doc/auto.html?highlight=automodelforctc#automodelforctc) for automatic speech 
+# Automatic Speech Recognition Examples
+
+## Table of Contents
+
+- [Automatic Speech Recognition with CTC](#connectionist-temporal-classification)
+	- [Single GPU example](#single-gpu)
+	- [Multi GPU example](#multi-gpu)
+	- [Examples](#examples)
+		- [TIMIT](#timit)
+		- [Librispeech](#librispeech)
+		- [Common Voice](#common-voice)
+		- [Multilingual Librispeech](#multilingual-librispeech)
+- [Automatic Speech Recognition with Sequence-to-Sequence](#sequence-to-sequence)
+	- [Single GPU example](#single-gpu)
+	- [Multi GPU example](#multi-gpu)
+	- [Examples](#examples)
+		- [Librispeech](#librispeech)
+
+## Connectionist Temporal Classification
+
+The script [`run_speech_recognition_ctc.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/run_speech_recognition_ctc.py) can be used to fine-tune any pretrained [Connectionist Temporal Classification Model](https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCTC) for automatic speech 
 recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset.
 
 Speech recognition models that have been pretrained in unsupervised fashion on audio data alone, *e.g.* [Wav2Vec2](https://huggingface.co/transformers/master/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/master/model_doc/hubert.html), [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html), have shown to require only 
@@ -41,7 +56,7 @@ If the environment variable is not set, the training script might freeze, *i.e.*
 
 ---
 
-### Single-GPU
+### Single GPU
 
 The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
 
@@ -75,7 +90,7 @@ python run_speech_recognition_ctc.py \
 On a single V100 GPU, this script should run in *ca.* 1 hour 20 minutes and yield a CTC loss of **0.39** and word error rate
 of **0.35**.
 
-### Multi-GPU
+### Multi GPU
 
 The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
 
@@ -92,7 +107,6 @@ python -m torch.distributed.launch \
 	--learning_rate="3e-4" \
 	--warmup_steps="500" \
 	--evaluation_strategy="steps" \
-	--audio_column_name="path" \
 	--text_column_name="sentence" \
 	--save_steps="400" \
 	--eval_steps="100" \
@@ -118,6 +132,8 @@ The presented performances are by no means optimal as no hyper-parameter tuning
 they can serve as a baseline to improve upon.
 
 
+#### TIMIT
+
 - [TIMIT](https://huggingface.co/datasets/timit_asr)
 
 | Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
@@ -129,6 +145,7 @@ they can serve as a baseline to improve upon.
 | [TIMIT](https://huggingface.co/datasets/timit_asr)| -  | [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) | 0.68 | - | 1 GPU TITAN RTX |  26min                      | [here](https://huggingface.co/patrickvonplaten/distilhubert-timit)  | [run.sh](https://huggingface.co/patrickvonplaten/distilhubert-timit/blob/main/run.sh) |
 
 
+#### Librispeech
 
 - [Librispeech](https://huggingface.co/datasets/librispeech_asr)
 
@@ -139,7 +156,10 @@ they can serve as a baseline to improve upon.
 | [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) | 0.042 | - | 8 GPU V100 | 1h30min  | [here](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
 | [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) | 0.042 | - | 8 GPU V100 | 1h30min  | [here](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
 | [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) | 0.088 | - | 8 GPU V100 | 1h30min  | [here](https://huggingface.co/patrickvonplaten/hubert-librispeech-clean-100h-demo-dist) | [run.sh](https://huggingface.co/patrickvonplaten/hubert-librispeech-clean-100h-demo-dist/blob/main/run.sh) |
-| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) | 0.167 | | | 8 GPU V100 | 54min  | [here](https://huggingface.co/patrickvonplaten/sew-mid-100k-librispeech-clean-100h-ft) | [run.sh](https://huggingface.co/patrickvonplaten/sew-mid-100k-librispeech-clean-100h-ft/blob/main/run.sh) |
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [asapp/sew-mid-100k](https://huggingface.co/asapp/sew-mid-100k) | 0.167 | | 8 GPU V100 | 54min  | [here](https://huggingface.co/patrickvonplaten/sew-mid-100k-librispeech-clean-100h-ft) | [run.sh](https://huggingface.co/patrickvonplaten/sew-mid-100k-librispeech-clean-100h-ft/blob/main/run.sh) |
+
+
+#### Common Voice
 
 - [Common Voice](https://huggingface.co/datasets/common_voice)
 
@@ -154,9 +174,196 @@ they can serve as a baseline to improve upon.
 | [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"`  | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)  | 0.31     | - | 8 GPU V100   |  1h05            | [here](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-common_voice-tr-ft)      |  [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-300m-common_voice-tr-ft/blob/main/run.sh) |
 | [Common Voice](https://huggingface.co/datasets/common_voice)| `"tr"`  | [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b)  | 0.21 | -  | 2 GPU Titan 24 GB RAM   |  15h10            | [here](https://huggingface.co/patrickvonplaten/wav2vec2-xls-r-1b-common_voice-tr-ft)      |  [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-large-xls-r-1b-common_voice-tr-ft/blob/main/run.sh) |
 
+
+#### Multilingual Librispeech
+
 - [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)
 
 | Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
 |-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
 | [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)| `"german"`  | [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)  | 0.13  | -     | 1 GPU Titan 24 GB RAM  |  15h04                 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-xlsr-53-300m-mls-german-ft)      |  [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-xlsr-53-300m-mls-german-ft/blob/main/run.sh) |
 | [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)| `"german"`  | [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m)  | 0.15 | -     | 1 GPU Titan 24 GB RAM  |  15h04                 | [here](https://huggingface.co/patrickvonplaten/wav2vec2-300m-mls-german-ft)      |  [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-300m-mls-german-ft/blob/main/run.sh) |
+
+## Sequence to Sequence
+
+The script [`run_speech_recognition_seq2seq.py`](https://github.com/huggingface/transformers/blob/master/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) can be used to fine-tune any [Speech Sequence-to-Sequence Model](https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForSpeechSeq2Seq) for automatic speech 
+recognition on one of the [official speech recognition datasets](https://huggingface.co/datasets?task_ids=task_ids:automatic-speech-recognition) or a custom dataset.
+
+A very common use case is to leverage a pretrained speech [encoding model](https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModel),
+*e.g.* [Wav2Vec2](https://huggingface.co/transformers/master/model_doc/wav2vec2.html), [HuBERT](https://huggingface.co/transformers/master/model_doc/hubert.html), [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) with a pretrained [text decoding model](https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModel), *e.g.* [Bart](https://huggingface.co/docs/transformers/master/en/model_doc/bart#transformers.BartForCausalLM) to create a [SpeechEnocderDecoderModel](https://huggingface.co/docs/transformers/master/en/model_doc/speechencoderdecoder#speech-encoder-decoder-models).
+Consequently, the warm-started Speech-Encoder-Decoder model can be fine-tuned in 
+this script.
+
+As an example, let's instantiate a *Wav2Vec2-2-Bart* model with the `SpeechEnocderDecoderModel` framework:
+
+First create an empty repo on `hf.co`:
+
+```bash
+huggingface-cli repo create wav2vec2-2-bart-base
+git clone https://huggingface.co/<your-user-name>/wav2vec2-2-bart-base
+cd wav2vec2-2-bart-base
+```
+
+Next, run the following script **inside** the just cloned repo:
+
+```py
+from transformers import SpeechEncoderDecoderModel, AutoFeatureExtractor, AutoTokenizer, Wav2Vec2Processor
+
+# checkpoints to leverage
+encoder_id = "facebook/wav2vec2-base"
+decoder_id = "facebook/bart-base"
+
+# load and save speech-encoder-decoder model
+# set some hyper-parameters for training and evaluation
+model = SpeechEncoderDecoderModel.from_encoder_decoder_pretrained(encoder_id, decoder_id, encoder_add_adapter=True, encoder_feat_proj_dropout=0.0, encoder_layerdrop=0.0, max_length=200, num_beams=5)
+model.config.decoder_start_token_id = model.decoder.config.bos_token_id
+model.config.pad_token_id = model.decoder.config.pad_token_id
+model.config.eos_token_id = model.decoder.config.eos_token_id
+model.save_pretrained("./")
+
+# load and save processor
+feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_id)
+tokenizer = AutoTokenizer.from_pretrained(decoder_id)
+processor = Wav2Vec2Processor(feature_extractor, tokenizer)
+processor.save_pretrained("./")
+```
+
+Finally, we can upload all files:
+```bash
+git lfs install
+git add . && git commit -m "upload model files" && git push
+```
+
+and link the official `run_speech_recognition_seq2seq.py` script to the folder:
+
+```bash
+ln -s $(realpath <path/to/transformers>/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py) ./
+```
+
+Note that we have added a randomly initialized adapter to `wav2vec2-base` with 
+`encoder_add_adapter=True` which further samples the output sequence of 
+`wav2vec2-base` along the time dimension. The reason is that by default a single
+output vector of `wav2vec2-base` has a receptive field of *ca.* 25ms (*cf.* with 
+section *4.2* of the [official Wav2Vec2 paper](https://arxiv.org/pdf/2006.11477.pdf)), which represents a little less a single character. BART on the other hand 
+makes use of a sentence-piece tokenizer as an input processor so that a single 
+hidden vector of `bart-base` represents *ca.* 4 characters. To better align 
+the output of *Wav2Vec2* and *BART*'s hidden vectors for the cross-attention 
+mechanism, we further subsample *Wav2Vec2*'s output by a factor of 8 by 
+adding a convolution-based adapter.
+
+Having warm-started the speech-encoder-decoder model `<your-user-name>/wav2vec2-2-bart`, we can now fine-tune it on speech recognition.
+
+In the script [`run_speech_recognition_seq2seq`], we load the warm-started model, 
+the feature extractor, and the tokenizer, process a speech recognition dataset, 
+and then make use of the [`Seq2SeqTrainer`](https://huggingface.co/docs/transformers/master/en/main_classes/trainer#transformers.Seq2SeqTrainer).
+Note that it is important to also align the decoder's vocabulary with 
+the speech transcriptions of the dataset. *E.g.* the [`Librispeech`](https://huggingface.co/datasets/librispeech_asr) has only captilized letters in the transcriptions,
+whereas BART was pretrained mostly on normalized text. Thus it is recommended to add
+`--do_lower_case` to the fine-tuning script when using a warm-started `SpeechEncoderDecoderModel`. The model is fine-tuned on the standard cross-entropy language modeling
+loss for sequence-to-sequence (just like *T5* or *BART* in natural language processing).
+
+---
+**NOTE**
+
+If you encounter problems with data preprocessing by setting `--preprocessing_num_workers` > 1, 
+you might want to set the environment variable `OMP_NUM_THREADS` to 1 as follows:
+
+```bash
+OMP_NUM_THREADS=1 python run_speech_recognition_ctc ...
+```
+
+If the environment variable is not set, the training script might freeze, *i.e.* see: https://github.com/pytorch/audio/issues/1021#issuecomment-726915239
+
+---
+
+### Single GPU
+
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using a single GPU in half-precision.
+
+```bash
+python run_speech_recognition_seq2seq.py \
+ 	--nproc_per_node 8 run_speech_recognition_seq2seq.py \
+	--dataset_name="librispeech_asr" \
+	--model_name_or_path="./" \
+	--dataset_config_name="clean" \
+	--train_split_name="train.100" \
+	--eval_split_name="validation" \
+	--output_dir="./" \
+	--preprocessing_num_workers="16" \
+	--length_column_name="input_length" \
+	--overwrite_output_dir \
+	--num_train_epochs="5" \
+	--per_device_train_batch_size="8" \
+	--per_device_eval_batch_size="8" \
+	--gradient_accumulation_steps="8" \
+	--learning_rate="3e-4" \
+	--warmup_steps="400" \
+	--evaluation_strategy="steps" \
+	--text_column_name="text" \
+	--save_steps="400" \
+	--eval_steps="400" \
+	--logging_steps="10" \
+	--save_total_limit="1" \
+	--freeze_feature_extractor \
+	--gradient_checkpointing \
+	--fp16 \
+	--group_by_length \
+	--predict_with_generate \
+	--generation_max_length="40" \
+	--generation_num_beams="1" \
+	--do_train --do_eval \
+	--do_lower_case
+```
+
+On a single V100 GPU, this script should run in *ca.* 5 hours and yield a 
+cross-entropy loss of **0.405** and word error rate of **0.0728**.
+
+### Multi GPU
+
+The following command shows how to fine-tune [XLSR-Wav2Vec2](https://huggingface.co/transformers/master/model_doc/xlsr_wav2vec2.html) on [Common Voice](https://huggingface.co/datasets/common_voice) using 8 GPUs in half-precision.
+
+```bash
+python -m torch.distributed.launch \
+ 	--nproc_per_node 8 run_speech_recognition_seq2seq.py \
+	--dataset_name="librispeech_asr" \
+	--model_name_or_path="./" \
+	--dataset_config_name="clean" \
+	--train_split_name="train.100" \
+	--eval_split_name="validation" \
+	--output_dir="./" \
+	--preprocessing_num_workers="16" \
+	--length_column_name="input_length" \
+	--overwrite_output_dir \
+	--num_train_epochs="5" \
+	--per_device_train_batch_size="8" \
+	--per_device_eval_batch_size="8" \
+	--gradient_accumulation_steps="1" \
+	--learning_rate="3e-4" \
+	--warmup_steps="400" \
+	--evaluation_strategy="steps" \
+	--text_column_name="text" \
+	--save_steps="400" \
+	--eval_steps="400" \
+	--logging_steps="10" \
+	--save_total_limit="1" \
+	--freeze_feature_extractor \
+	--gradient_checkpointing \
+	--fp16 \
+	--group_by_length \
+	--predict_with_generate \
+	--do_train --do_eval \
+	--do_lower_case
+```
+
+On 8 V100 GPUs, this script should run in *ca.* 45 minutes and yield a cross-entropy loss of **0.405** and word error rate of **0.0728**
+
+### Examples
+
+#### Librispeech
+
+- [Librispeech](https://huggingface.co/datasets/librispeech_asr)
+
+| Dataset | Dataset Config | Pretrained Model | Word error rate on eval | Phoneme error rate on eval | GPU setup | Training time | Fine-tuned Model & Logs | Command to reproduce |
+|-------|------------------------------|-------------|---------------|---------------|----------------------|-------------| -------------| ------- |
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) and [facebook/bart-base](https://huggingface.co/facebook/bart-base) | 0.0728 | - | 8 GPU V100 | 45min  | [here](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base) | [create_model.py](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base/blob/main/create_model.py) & [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-base/blob/main/run_librispeech.sh) |
+| [Librispeech](https://huggingface.co/datasets/librispeech_asr)| `"clean"` - `"train.100"` |  [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) and [facebook/bart-large](https://huggingface.co/facebook/bart-large) | 0.0486 | - | 8 GPU V100 | 1h20min  | [here](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large) | [create_model.py](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large/blob/main/create_model.py) & [run.sh](https://huggingface.co/patrickvonplaten/wav2vec2-2-bart-large/blob/main/run_librispeech.sh) |

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

@@ -635,14 +635,13 @@ def main():
 
         return metrics
 
-    # Now create a single processor
+    # Now save everything to be able to create a single processor later
     if is_main_process(training_args.local_rank):
         # save feature extractor, tokenizer and config
         feature_extractor.save_pretrained(training_args.output_dir)
         tokenizer.save_pretrained(training_args.output_dir)
         config.save_pretrained(training_args.output_dir)
 
-    # load processor
     try:
         processor = AutoProcessor.from_pretrained(training_args.output_dir)
     except (OSError, KeyError):

examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021 The HuggingFace 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.
+"""
+Fine-tuning the library models for sequence to sequence speech recognition.
+"""
+# You can also adapt this script on your own sequence to sequence speech
+# recognition task. Pointers for this are left as comments.
+
+import logging
+import os
+import sys
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Union
+
+import datasets
+import torch
+from datasets import DatasetDict, load_dataset, load_metric
+
+import transformers
+from transformers import (
+    AutoConfig,
+    AutoFeatureExtractor,
+    AutoModelForSpeechSeq2Seq,
+    AutoProcessor,
+    AutoTokenizer,
+    HfArgumentParser,
+    Seq2SeqTrainer,
+    Seq2SeqTrainingArguments,
+    set_seed,
+)
+from transformers.trainer_utils import get_last_checkpoint, is_main_process
+from transformers.utils import check_min_version
+from transformers.utils.versions import require_version
+
+
+# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
+check_min_version("4.16.0.dev0")
+
+require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
+    """
+
+    model_name_or_path: str = field(
+        metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
+    )
+    config_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
+    )
+    feature_extractor_name: Optional[str] = field(
+        default=None, metadata={"help": "feature extractor name or path if not the same as model_name"}
+    )
+    cache_dir: Optional[str] = field(
+        default=None,
+        metadata={"help": "Where to store the pretrained models downloaded from huggingface.co"},
+    )
+    use_fast_tokenizer: bool = field(
+        default=True,
+        metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
+    )
+    model_revision: str = field(
+        default="main",
+        metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
+    )
+    use_auth_token: bool = field(
+        default=False,
+        metadata={
+            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
+            "with private models)."
+        },
+    )
+    freeze_feature_extractor: Optional[bool] = field(
+        default=True, metadata={"help": "Whether to freeze the feature extractor layers of the model."}
+    )
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    dataset_name: Optional[str] = field(
+        default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
+    )
+    dataset_config_name: Optional[str] = field(
+        default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
+    )
+    text_column: Optional[str] = field(
+        default=None,
+        metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
+    )
+    overwrite_cache: bool = field(
+        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={"help": "The number of processes to use for the preprocessing."},
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
+            "value if set."
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
+            "value if set."
+        },
+    )
+    audio_column_name: Optional[str] = field(
+        default="audio",
+        metadata={"help": "The name of the dataset column containing the audio data. Defaults to 'audio'"},
+    )
+    text_column_name: Optional[str] = field(
+        default="text",
+        metadata={"help": "The name of the dataset column containing the text data. Defaults to 'text'"},
+    )
+    max_duration_in_seconds: Optional[float] = field(
+        default=20.0,
+        metadata={
+            "help": "Truncate audio files that are longer than `max_duration_in_seconds` seconds to 'max_duration_in_seconds`"
+        },
+    )
+    min_duration_in_seconds: Optional[float] = field(
+        default=0.0, metadata={"help": "Filter audio files that are shorter than `min_duration_in_seconds` seconds"}
+    )
+    preprocessing_only: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether to only do data preprocessing and skip training. "
+            "This is especially useful when data preprocessing errors out in distributed training due to timeout. "
+            "In this case, one should run the preprocessing in a non-distributed setup with `preprocessing_only=True` "
+            "so that the cached datasets can consequently be loaded in distributed training"
+        },
+    )
+    train_split_name: Optional[str] = field(
+        default="train",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    eval_split_name: Optional[str] = field(
+        default="test",
+        metadata={
+            "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
+        },
+    )
+    do_lower_case: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether the target text should be lower cased."},
+    )
+
+
+@dataclass
+class DataCollatorSpeechSeq2SeqWithPadding:
+    """
+    Data collator that will dynamically pad the inputs received.
+    Args:
+        processor ([`Wav2Vec2Processor`])
+            The processor used for proccessing the data.
+        decoder_start_token_id (`int`)
+            The begin-of-sentence of the decoder.
+    """
+
+    processor: Any
+    decoder_start_token_id: int
+
+    def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
+        # split inputs and labels since they have to be of different lenghts and need
+        # different padding methods
+        input_features = [{"input_values": feature["input_values"]} for feature in features]
+        label_features = [{"input_ids": feature["labels"]} for feature in features]
+
+        batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
+
+        labels_batch = self.processor.tokenizer.pad(label_features, return_tensors="pt")
+
+        # replace padding with -100 to ignore loss correctly
+        labels = labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1), -100)
+
+        # if bos token is appended in previous tokenization step,
+        # cut bos token here as it's append later anyways
+        if (labels[:, 0] == self.decoder_start_token_id).all().cpu().item():
+            labels = labels[:, 1:]
+
+        batch["labels"] = labels
+
+        return batch
+
+
+def main():
+    # 1. Parse input arguments
+    # See all possible arguments in src/transformers/training_args.py
+    # or by passing the --help flag to this script.
+    # We now keep distinct sets of args, for a cleaner separation of concerns.
+    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
+
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # 2. Setup logging
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        handlers=[logging.StreamHandler(sys.stdout)],
+    )
+    log_level = training_args.get_process_log_level()
+    logger.setLevel(log_level)
+    datasets.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.set_verbosity(log_level)
+    transformers.utils.logging.enable_default_handler()
+    transformers.utils.logging.enable_explicit_format()
+
+    logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN)
+
+    # Log on each process the small summary:
+    logger.warning(
+        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+        f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
+    )
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    if is_main_process(training_args.local_rank):
+        transformers.utils.logging.set_verbosity_info()
+    logger.info("Training/evaluation parameters %s", training_args)
+
+    # 3. Detecting last checkpoint and eventualy continue from last checkpoint
+    last_checkpoint = None
+    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
+        last_checkpoint = get_last_checkpoint(training_args.output_dir)
+        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
+            raise ValueError(
+                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
+                "Use --overwrite_output_dir to overcome."
+            )
+        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
+            logger.info(
+                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
+                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
+            )
+
+    # Set seed before initializing model.
+    set_seed(training_args.seed)
+
+    # 4. Load dataset
+    raw_datasets = DatasetDict()
+
+    if training_args.do_train:
+        raw_datasets["train"] = load_dataset(
+            data_args.dataset_name, data_args.dataset_config_name, split=data_args.train_split_name
+        )
+
+    if training_args.do_eval:
+        raw_datasets["eval"] = load_dataset(
+            data_args.dataset_name, data_args.dataset_config_name, split=data_args.eval_split_name
+        )
+
+    if data_args.audio_column_name not in next(iter(raw_datasets.values())).column_names:
+        raise ValueError(
+            f"--audio_column_name '{data_args.audio_column_name}' not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--audio_column_name` to the correct audio column - one of "
+            f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
+        )
+
+    if data_args.text_column_name not in next(iter(raw_datasets.values())).column_names:
+        raise ValueError(
+            f"--text_column_name {data_args.text_column_name} not found in dataset '{data_args.dataset_name}'. "
+            "Make sure to set `--text_column_name` to the correct text column - one of "
+            f"{', '.join(next(iter(raw_datasets.values())).column_names)}."
+        )
+
+    # 5. Load pretrained model, tokenizer, and feature extractor
+    #
+    # Distributed training:
+    # The .from_pretrained methods guarantee that only one local process can concurrently
+    config = AutoConfig.from_pretrained(
+        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    feature_extractor = AutoFeatureExtractor.from_pretrained(
+        model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
+        cache_dir=model_args.cache_dir,
+        use_fast=model_args.use_fast_tokenizer,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+    model = AutoModelForSpeechSeq2Seq.from_pretrained(
+        model_args.model_name_or_path,
+        config=config,
+        cache_dir=model_args.cache_dir,
+        revision=model_args.model_revision,
+        use_auth_token=True if model_args.use_auth_token else None,
+    )
+
+    if model.config.decoder_start_token_id is None:
+        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")
+
+    if model_args.freeze_feature_extractor:
+        model.freeze_feature_extractor()
+
+    # 6. Resample speech dataset if necassary
+    dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
+    if dataset_sampling_rate != feature_extractor.sampling_rate:
+        raw_datasets = raw_datasets.cast_column(
+            data_args.audio_column_name, datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate)
+        )
+
+    # 7. Preprocessing the datasets.
+    # We need to read the audio files as arrays and tokenize the targets.
+    max_input_length = data_args.max_duration_in_seconds * feature_extractor.sampling_rate
+    min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
+    audio_column_name = data_args.audio_column_name
+    num_workers = data_args.preprocessing_num_workers
+    text_column_name = data_args.text_column_name
+    model_input_name = feature_extractor.model_input_names[0]
+    do_lower_case = data_args.do_lower_case
+
+    if data_args.max_train_samples is not None:
+        raw_datasets["train"] = raw_datasets["train"].select(range(data_args.max_train_samples))
+
+    if data_args.max_eval_samples is not None:
+        raw_datasets["eval"] = raw_datasets["eval"].select(range(data_args.max_train_samples))
+
+    def prepare_dataset(batch):
+        # process audio
+        sample = batch[audio_column_name]
+        inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
+        # process audio length
+        batch[model_input_name] = inputs.input_values[0]
+        batch["input_length"] = len(batch["input_values"])
+
+        # process targets
+        input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name]
+        batch["labels"] = tokenizer(input_str).input_ids
+        return batch
+
+    with training_args.main_process_first(desc="dataset map pre-processing"):
+        vectorized_datasets = raw_datasets.map(
+            prepare_dataset,
+            remove_columns=next(iter(raw_datasets.values())).column_names,
+            num_proc=data_args.preprocessing_num_workers,
+            desc="preprocess train dataset",
+        )
+
+    # filter data that is shorter than min_input_length or longer than
+    # max_input_length
+    def is_audio_in_length_range(length):
+        return length > min_input_length and length < max_input_length
+
+    vectorized_datasets = vectorized_datasets.filter(
+        is_audio_in_length_range,
+        num_proc=num_workers,
+        input_columns=["input_length"],
+    )
+
+    # for large datasets it is advised to run the preprocessing on a
+    # single machine first with `args.preprocessing_only` since there will mostly likely
+    # be a timeout when running the script in distributed mode.
+    # In a second step `args.preprocessing_only` can then be set to `False` to load the
+    # cached dataset
+    if data_args.preprocessing_only:
+        cache = {k: v.cache_files for k, v in vectorized_datasets.items()}
+        logger.info(f"Data preprocessing finished. Files cached at {cache}.")
+        return
+
+    # 8. Load Metric
+    metric = load_metric("wer")
+
+    def compute_metrics(pred):
+        pred_ids = pred.predictions
+
+        pred.label_ids[pred.label_ids == -100] = tokenizer.pad_token_id
+
+        pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)
+        # we do not want to group tokens when computing the metrics
+        label_str = tokenizer.batch_decode(pred.label_ids, skip_special_tokens=True)
+
+        wer = metric.compute(predictions=pred_str, references=label_str)
+
+        return {"wer": wer}
+
+    # 9. Create a single speech processor
+    if is_main_process(training_args.local_rank):
+        # save feature extractor, tokenizer and config
+        feature_extractor.save_pretrained(training_args.output_dir)
+        tokenizer.save_pretrained(training_args.output_dir)
+        config.save_pretrained(training_args.output_dir)
+
+    processor = AutoProcessor.from_pretrained(training_args.output_dir)
+
+    # 10. Define data collator
+    data_collator = DataCollatorSpeechSeq2SeqWithPadding(
+        processor=processor, decoder_start_token_id=model.config.decoder_start_token_id
+    )
+
+    # 11. Initialize Trainer
+    trainer = Seq2SeqTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=vectorized_datasets["train"] if training_args.do_train else None,
+        eval_dataset=vectorized_datasets["eval"] if training_args.do_eval else None,
+        tokenizer=feature_extractor,
+        data_collator=data_collator,
+        compute_metrics=compute_metrics if training_args.predict_with_generate else None,
+    )
+
+    # 12. Training
+    if training_args.do_train:
+        checkpoint = None
+        if training_args.resume_from_checkpoint is not None:
+            checkpoint = training_args.resume_from_checkpoint
+        elif last_checkpoint is not None:
+            checkpoint = last_checkpoint
+        train_result = trainer.train(resume_from_checkpoint=checkpoint)
+        trainer.save_model()  # Saves the feature extractor too for easy upload
+
+        metrics = train_result.metrics
+        max_train_samples = (
+            data_args.max_train_samples
+            if data_args.max_train_samples is not None
+            else len(vectorized_datasets["train"])
+        )
+        metrics["train_samples"] = min(max_train_samples, len(vectorized_datasets["train"]))
+        trainer.log_metrics("train", metrics)
+        trainer.save_metrics("train", metrics)
+        trainer.save_state()
+
+    # 13. Evaluation
+    results = {}
+    if training_args.do_eval:
+        logger.info("*** Evaluate ***")
+        metrics = trainer.evaluate(
+            metric_key_prefix="eval", max_length=model.config.max_length, num_beams=model.config.num_beams
+        )
+        max_eval_samples = (
+            data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])
+        )
+        metrics["eval_samples"] = min(max_eval_samples, len(vectorized_datasets["eval"]))
+
+        trainer.log_metrics("eval", metrics)
+        trainer.save_metrics("eval", metrics)
+
+    # 14. Write Training Stats
+    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "speech recognition"}
+    if data_args.dataset_name is not None:
+        kwargs["dataset_tags"] = data_args.dataset_name
+        if data_args.dataset_config_name is not None:
+            kwargs["dataset_args"] = data_args.dataset_config_name
+            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
+        else:
+            kwargs["dataset"] = data_args.dataset_name
+
+    if training_args.push_to_hub:
+        trainer.push_to_hub(**kwargs)
+    else:
+        trainer.create_model_card(**kwargs)
+
+    return results
+
+
+if __name__ == "__main__":
+    main()

examples/pytorch/test_examples.py

@@ -59,6 +59,7 @@ if SRC_DIRS is not None:
     import run_qa as run_squad
     import run_seq2seq_qa as run_squad_seq2seq
     import run_speech_recognition_ctc
+    import run_speech_recognition_seq2seq
     import run_summarization
     import run_swag
     import run_translation
@@ -473,6 +474,39 @@ class ExamplesTests(TestCasePlus):
             result = get_results(tmp_dir)
             self.assertLess(result["eval_loss"], result["train_loss"])
 
+    def test_run_speech_recognition_seq2seq(self):
+        stream_handler = logging.StreamHandler(sys.stdout)
+        logger.addHandler(stream_handler)
+
+        tmp_dir = self.get_auto_remove_tmp_dir()
+        testargs = f"""
+            run_speech_recognition_seq2seq.py
+            --output_dir {tmp_dir}
+            --model_name_or_path hf-internal-testing/tiny-random-speech-encoder-decoder
+            --dataset_name hf-internal-testing/librispeech_asr_dummy
+            --dataset_config_name clean
+            --train_split_name validation
+            --eval_split_name validation
+            --do_train
+            --do_eval
+            --learning_rate 1e-4
+            --per_device_train_batch_size 2
+            --per_device_eval_batch_size 4
+            --remove_unused_columns False
+            --overwrite_output_dir True
+            --preprocessing_num_workers 16
+            --max_steps 10
+            --seed 42
+        """.split()
+
+        if is_cuda_and_apex_available():
+            testargs.append("--fp16")
+
+        with patch.object(sys, "argv", testargs):
+            run_speech_recognition_seq2seq.main()
+            result = get_results(tmp_dir)
+            self.assertLess(result["eval_loss"], result["train_loss"])
+
     def test_run_audio_classification(self):
         stream_handler = logging.StreamHandler(sys.stdout)
         logger.addHandler(stream_handler)
@@ -521,10 +555,10 @@ class ExamplesTests(TestCasePlus):
             --dataset_config_names clean
             --dataset_split_names validation
             --learning_rate 1e-4
-            --per_device_train_batch_size 2
-            --per_device_eval_batch_size 2
+            --per_device_train_batch_size 4
+            --per_device_eval_batch_size 4
             --preprocessing_num_workers 16
-            --max_train_steps 5
+            --max_train_steps 2
             --validation_split_percentage 5
             --seed 42
         """.split()

src/transformers/models/auto/processing_auto.py

@@ -164,7 +164,7 @@ class AutoProcessor:
         model_type = config_class_to_model_type(type(config).__name__)
 
         if getattr(config, "processor_class", None) is not None:
-            processor_class = config.processor_class
+            processor_class = processor_class_from_name(config.processor_class)
             return processor_class.from_pretrained(pretrained_model_name_or_path, **kwargs)
 
         model_type = config_class_to_model_type(type(config).__name__)

src/transformers/models/hubert/modeling_hubert.py

@@ -905,7 +905,8 @@ class HubertModel(HubertPreTrainedModel):
         self.feature_extractor = HubertFeatureExtractor(config)
         self.feature_projection = HubertFeatureProjection(config)
 
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
+        if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
+            self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
 
         if config.do_stable_layer_norm:
             self.encoder = HubertEncoderStableLayerNorm(config)

src/transformers/models/sew/modeling_sew.py

@@ -805,7 +805,8 @@ class SEWModel(SEWPreTrainedModel):
             self.feature_projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
         self.feature_dropout = nn.Dropout(config.feat_proj_dropout)
 
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
+        if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
+            self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
 
         self.encoder = SEWEncoder(config)
 

src/transformers/models/sew_d/modeling_sew_d.py

@@ -1341,7 +1341,8 @@ class SEWDModel(SEWDPreTrainedModel):
             self.feature_projection = nn.Linear(config.conv_dim[-1], config.hidden_size)
         self.feature_dropout = nn.Dropout(config.feat_proj_dropout)
 
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
+        if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
+            self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
 
         self.encoder = SEWDEncoder(config)
 

src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py

@@ -181,6 +181,7 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
     config_class = SpeechEncoderDecoderConfig
     base_model_prefix = "speech_encoder_decoder"
     main_input_name = "inputs"
+    supports_gradient_checkpointing = True
 
     def __init__(
         self,
@@ -247,6 +248,11 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
                 f"The encoder {self.encoder} should not have a LM Head. Please use a model without LM Head"
             )
 
+    def _set_gradient_checkpointing(self, module, value=False):
+        # call both encoder and decoder function on gradient checkpointing
+        self.encoder._set_gradient_checkpointing(module, value=value)
+        self.decoder._set_gradient_checkpointing(module, value=value)
+
     def get_encoder(self):
         return self.encoder
 
@@ -259,6 +265,13 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
     def set_output_embeddings(self, new_embeddings):
         return self.decoder.set_output_embeddings(new_embeddings)
 
+    def freeze_feature_extractor(self):
+        """
+        Calling this function will disable the gradient computation for the feature extractor of the speech encoder so
+        that its parameters will not be updated during training.
+        """
+        self.encoder.freeze_feature_extractor()
+
     @classmethod
     def from_pretrained(cls, *args, **kwargs):
         # At the moment fast initialization is not supported for composite models
@@ -367,7 +380,7 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
                 )
 
             if "config" not in kwargs_encoder:
-                encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path)
+                encoder_config = AutoConfig.from_pretrained(encoder_pretrained_model_name_or_path, **kwargs_encoder)
                 if encoder_config.is_decoder is True or encoder_config.add_cross_attention is True:
                     logger.info(
                         f"Initializing {encoder_pretrained_model_name_or_path} as a encoder model "
@@ -378,7 +391,7 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
 
                 kwargs_encoder["config"] = encoder_config
 
-            encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args, **kwargs_encoder)
+            encoder = AutoModel.from_pretrained(encoder_pretrained_model_name_or_path, *model_args)
 
         decoder = kwargs_decoder.pop("model", None)
         if decoder is None:
@@ -389,7 +402,7 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
                 )
 
             if "config" not in kwargs_decoder:
-                decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path)
+                decoder_config = AutoConfig.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
                 if decoder_config.is_decoder is False or decoder_config.add_cross_attention is False:
                     logger.info(
                         f"Initializing {decoder_pretrained_model_name_or_path} as a decoder model. "
@@ -411,7 +424,7 @@ class SpeechEncoderDecoderModel(PreTrainedModel):
                     "`decoder_config` to `.from_encoder_decoder_pretrained(...)`"
                 )
 
-            decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path, **kwargs_decoder)
+            decoder = AutoModelForCausalLM.from_pretrained(decoder_pretrained_model_name_or_path)
 
         # instantiate config with corresponding kwargs
         config = SpeechEncoderDecoderConfig.from_encoder_decoder_configs(encoder.config, decoder.config, **kwargs)

src/transformers/models/unispeech/modeling_unispeech.py

@@ -1052,7 +1052,8 @@ class UniSpeechModel(UniSpeechPreTrainedModel):
         self.feature_extractor = UniSpeechFeatureExtractor(config)
         self.feature_projection = UniSpeechFeatureProjection(config)
 
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
+        if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
+            self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
 
         if config.do_stable_layer_norm:
             self.encoder = UniSpeechEncoderStableLayerNorm(config)

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -1197,7 +1197,9 @@ class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
         self.feature_extractor = Wav2Vec2FeatureExtractor(config)
         self.feature_projection = Wav2Vec2FeatureProjection(config)
 
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
+        # model only needs masking vector if mask prob is > 0.0
+        if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
+            self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
 
         if config.do_stable_layer_norm:
             self.encoder = Wav2Vec2EncoderStableLayerNorm(config)
@@ -1209,6 +1211,13 @@ class Wav2Vec2Model(Wav2Vec2PreTrainedModel):
         # Initialize weights and apply final processing
         self.post_init()
 
+    def freeze_feature_extractor(self):
+        """
+        Calling this function will disable the gradient computation for the feature extractor so that its parameters
+        will not be updated during training.
+        """
+        self.feature_extractor._freeze_parameters()
+
     def _mask_hidden_states(
         self,
         hidden_states: torch.FloatTensor,

src/transformers/models/wav2vec2/processing_wav2vec2.py

@@ -19,6 +19,7 @@ import warnings
 from contextlib import contextmanager
 
 from ...tokenization_utils import PreTrainedTokenizer
+from ...tokenization_utils_fast import PreTrainedTokenizerFast
 from ..auto.tokenization_auto import AutoTokenizer
 from .feature_extraction_wav2vec2 import Wav2Vec2FeatureExtractor
 from .tokenization_wav2vec2 import Wav2Vec2CTCTokenizer
@@ -44,7 +45,7 @@ class Wav2Vec2Processor:
             raise ValueError(
                 f"`feature_extractor` has to be of type {Wav2Vec2FeatureExtractor.__class__}, but is {type(feature_extractor)}"
             )
-        if not isinstance(tokenizer, PreTrainedTokenizer):
+        if not isinstance(tokenizer, (PreTrainedTokenizer, PreTrainedTokenizerFast)):
             raise ValueError(
                 f"`tokenizer` has to be of type {PreTrainedTokenizer.__class__}, but is {type(tokenizer)}"
             )

src/transformers/models/wavlm/modeling_wavlm.py

@@ -1149,7 +1149,9 @@ class WavLMModel(WavLMPreTrainedModel):
         self.feature_extractor = WavLMFeatureExtractor(config)
         self.feature_projection = WavLMFeatureProjection(config)
 
-        self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
+        # model only needs masking vector if mask prob is > 0.0
+        if config.mask_time_prob > 0.0 or config.mask_feature_prob > 0.0:
+            self.masked_spec_embed = nn.Parameter(torch.FloatTensor(config.hidden_size).uniform_())
 
         if config.do_stable_layer_norm:
             self.encoder = WavLMEncoderStableLayerNorm(config)
@@ -1161,6 +1163,13 @@ class WavLMModel(WavLMPreTrainedModel):
         # Initialize weights and apply final processing
         self.post_init()
 
+    def freeze_feature_extractor(self):
+        """
+        Calling this function will disable the gradient computation for the feature extractor so that its parameters
+        will not be updated during training.
+        """
+        self.feature_extractor._freeze_parameters()
+
     def _mask_hidden_states(
         self,
         hidden_states: torch.FloatTensor,