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@ -19,18 +19,18 @@ limitations under the License.
## 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)
- [Single GPU example](#single-gpu-ctc)
- [Multi GPU example](#multi-gpu-ctc)
- [Examples](#examples-ctc)
- [TIMIT](#timit-ctc)
- [Librispeech](#librispeech-ctc)
- [Common Voice](#common-voice-ctc)
- [Multilingual Librispeech](#multilingual-librispeech-ctc)
- [Automatic Speech Recognition with Sequence-to-Sequence](#sequence-to-sequence)
- [Single GPU example](#single-gpu)
- [Multi GPU example](#multi-gpu)
- [Examples](#examples)
- [Librispeech](#librispeech)
- [Single GPU example](#single-gpu-seq2seq)
- [Multi GPU example](#multi-gpu-seq2seq)
- [Examples](#examples-seq2seq)
- [Librispeech](#librispeech-seq2seq)
## Connectionist Temporal Classification
@ -56,7 +56,7 @@ If the environment variable is not set, the training script might freeze, *i.e.*
---
### Single GPU
### Single GPU CTC
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.
@ -90,7 +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 CTC
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.
@ -125,14 +125,14 @@ python -m torch.distributed.launch \
On 8 V100 GPUs, this script should run in *ca.* 18 minutes and yield a CTC loss of **0.39** and word error rate
of **0.36**.
### Examples
### Examples CTC
The following tables present a couple of example runs on the most popular speech-recognition datasets.
The presented performances are by no means optimal as no hyper-parameter tuning was done. Nevertheless,
they can serve as a baseline to improve upon.
#### TIMIT
#### TIMIT CTC
- [TIMIT](https://huggingface.co/datasets/timit_asr)
@ -145,7 +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 CTC
- [Librispeech](https://huggingface.co/datasets/librispeech_asr)
@ -159,7 +159,7 @@ they can serve as a baseline to improve upon.
| [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 CTC
- [Common Voice](https://huggingface.co/datasets/common_voice)
@ -175,7 +175,7 @@ they can serve as a baseline to improve upon.
| [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 CTC
- [Multilingual Librispeech](https://huggingface.co/datasets/multilingual_librispeech)
@ -276,7 +276,7 @@ If the environment variable is not set, the training script might freeze, *i.e.*
---
### Single GPU
### Single GPU Seq2Seq
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.
@ -318,7 +318,7 @@ python run_speech_recognition_seq2seq.py \
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
### Multi GPU Seq2Seq
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.
@ -357,9 +357,9 @@ python -m torch.distributed.launch \
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
### Examples Seq2Seq
#### Librispeech
#### Librispeech Seq2Seq
- [Librispeech](https://huggingface.co/datasets/librispeech_asr)