Open-source model wav2vec2-xls-r-300m-german-de - accurately implement automatic German speech recognition

Wav2vec2 Xls R 300m German De

Developed by AndrewMcDowell

This model is a fine-tuned German automatic speech recognition (ASR) model based on facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - DE dataset.

Speech Recognition

Transformers

GermanOpen Source License:Apache-2.0 #German Speech Recognition #Multi-scenario Adaptation #Low CER Optimization

Downloads 72

Release Time : 3/2/2022

Model Overview

This is an optimized automatic speech recognition (ASR) model for German, based on the XLS-R-300M architecture and fine-tuned on the Common Voice 7.0 German dataset.

Model Features

German Optimization

Specially fine-tuned and optimized for German speech recognition

Based on XLS-R Architecture

Uses facebook's wav2vec2-xls-r-300m pre-trained model as the foundation

Multi-dataset Evaluation

Evaluated on multiple datasets including Common Voice 7 and robust speech challenges

Model Capabilities

German Speech-to-Text

Continuous Speech Recognition

Speech Content Transcription

Use Cases

Speech Transcription

German Speech Transcription

Convert German speech content into text

Achieves a WER of 20.16% on the Common Voice 7 test set

Voice Assistants

German Voice Command Recognition

Used as a speech recognition component for German voice assistants or voice control systems

🚀 XLS-R-300M - German

This model is a fine - tuned version of [facebook/wav2vec2 - xls - r - 300m](https://huggingface.co/facebook/wav2vec2 - xls - r - 300m) on the German dataset of MOZILLA - FOUNDATION/COMMON_VOICE_7_0. It provides high - quality automatic speech recognition for the German language.

🚀 Quick Start

This model is a fine - tuned version of [facebook/wav2vec2 - xls - r - 300m](https://huggingface.co/facebook/wav2vec2 - xls - r - 300m) on the MOZILLA - FOUNDATION/COMMON_VOICE_7_0 - DE dataset. It achieves the following results on the evaluation set:

Loss: 0.1768
Wer: 0.2016

✨ Features

Fine - tuned: Based on the pre - trained model [facebook/wav2vec2 - xls - r - 300m](https://huggingface.co/facebook/wav2vec2 - xls - r - 300m), fine - tuned on German speech data.
Multilingual Adaptability: Leveraging the power of XLS - R architecture, it has potential adaptability for multilingual scenarios.
Good Performance: Achieved relatively low WER and CER on the evaluation datasets.

📦 Installation

No specific installation steps are provided in the original document.

💻 Usage Examples

No code examples are provided in the original document.

📚 Documentation

Model Information

Property	Details
Model Type	XLS - R - 300M - German
Training Data	mozilla - foundation/common_voice_7_0 (German subset)

Model Results

Task	Dataset	Test WER	Test CER
Automatic Speech Recognition	Common Voice 7 (de)	20.16	5.06
Automatic Speech Recognition	Robust Speech Event - Dev Data (de)	39.79	15.02
Automatic Speech Recognition	Robust Speech Event - Test Data (de)	47.95	N/A

Training and Evaluation

Training Hyperparameters

The following hyperparameters were used during training:

learning_rate: 7.5e - 05
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 32
optimizer: Adam with betas=(0.9, 0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2000
num_epochs: 3.4
mixed_precision_training: Native AMP

Training Results

Training Loss	Epoch	Step	Validation Loss	Wer
5.7531	0.04	500	5.4564	1.0
2.9882	0.08	1000	3.0041	1.0
2.1953	0.13	1500	1.1723	0.7121
1.2406	0.17	2000	0.3656	0.3623
1.1294	0.21	2500	0.2843	0.2926
1.0731	0.25	3000	0.2554	0.2664
1.051	0.3	3500	0.2387	0.2535
1.0479	0.34	4000	0.2345	0.2512
1.0026	0.38	4500	0.2270	0.2452
0.9921	0.42	5000	0.2212	0.2353
0.9839	0.47	5500	0.2141	0.2330
0.9907	0.51	6000	0.2122	0.2334
0.9788	0.55	6500	0.2114	0.2270
0.9687	0.59	7000	0.2066	0.2323
0.9777	0.64	7500	0.2033	0.2237
0.9476	0.68	8000	0.2020	0.2194
0.9625	0.72	8500	0.1977	0.2191
0.9497	0.76	9000	0.1976	0.2175
0.9781	0.81	9500	0.1956	0.2159
0.9552	0.85	10000	0.1958	0.2191
0.9345	0.89	10500	0.1964	0.2158
0.9528	0.93	11000	0.1926	0.2154
0.9502	0.98	11500	0.1953	0.2149
0.9358	1.02	12000	0.1927	0.2167
0.941	1.06	12500	0.1901	0.2115
0.9287	1.1	13000	0.1936	0.2090
0.9491	1.15	13500	0.1900	0.2104
0.9478	1.19	14000	0.1931	0.2120
0.946	1.23	14500	0.1914	0.2134
0.9499	1.27	15000	0.1931	0.2173
0.9346	1.32	15500	0.1913	0.2105
0.9509	1.36	16000	0.1902	0.2137
0.9294	1.4	16500	0.1895	0.2086
0.9418	1.44	17000	0.1913	0.2183
0.9302	1.49	17500	0.1884	0.2114
0.9418	1.53	18000	0.1894	0.2108
0.9363	1.57	18500	0.1886	0.2132
0.9338	1.61	19000	0.1856	0.2078
0.9185	1.66	19500	0.1852	0.2056
0.9216	1.7	20000	0.1874	0.2095
0.9176	1.74	20500	0.1873	0.2078
0.9288	1.78	21000	0.1865	0.2097
0.9278	1.83	21500	0.1869	0.2100
0.9295	1.87	22000	0.1878	0.2095
0.9221	1.91	22500	0.1852	0.2121
0.924	1.95	23000	0.1855	0.2042
0.9104	2.0	23500	0.1858	0.2105
0.9284	2.04	24000	0.1850	0.2080
0.9162	2.08	24500	0.1839	0.2045
0.9111	2.12	25000	0.1838	0.2080
0.91	2.17	25500	0.1889	0.2106
0.9152	2.21	26000	0.1856	0.2026
0.9209	2.25	26500	0.1891	0.2133
0.9094	2.29	27000	0.1857	0.2089
0.9065	2.34	27500	0.1840	0.2052
0.9156	2.38	28000	0.1833	0.2062
0.8986	2.42	28500	0.1789	0.2001
0.9045	2.46	29000	0.1769	0.2022
0.9039	2.51	29500	0.1819	0.2073
0.9145	2.55	30000	0.1828	0.2063
0.9081	2.59	30500	0.1811	0.2049
0.9252	2.63	31000	0.1833	0.2086
0.8957	2.68	31500	0.1795	0.2083
0.891	2.72	32000	0.1809	0.2058
0.9023	2.76	32500	0.1812	0.2061
0.8918	2.8	33000	0.1775	0.1997
0.8852	2.85	33500	0.1790	0.1997
0.8928	2.89	34000	0.1767	0.2013
0.9079	2.93	34500	0.1735	0.1986
0.9032	2.97	35000	0.1793	0.2024
0.9018	3.02	35500	0.1778	0.2027
0.8846	3.06	36000	0.1776	0.2046
0.8848	3.1	36500	0.1812	0.2064
0.9062	3.14	37000	0.1800	0.2018
0.9011	3.19	37500	0.1783	0.2049
0.8996	3.23	38000	0.1810	0.2036
0.893	3.27	38500	0.1805	0.2056
0.897	3.31	39000	0.1773	0.2035
0.8992	3.36	39500	0.1804	0.2054
0.8987	3.4	40000	0.1768	0.2016

Framework Versions

Transformers 4.16.0.dev0
Pytorch 1.10.1+cu102
Datasets 1.17.1.dev0
Tokenizers 0.11.0

Evaluation Commands

To evaluate on mozilla - foundation/common_voice_7_0 with split test

python ./eval.py --model_id AndrewMcDowell/wav2vec2 - xls - r - 300m - german - de --dataset mozilla - foundation/common_voice_7_0 --config de --split test --log_outputs

To evaluate on test dev data

python ./eval.py --model_id AndrewMcDowell/wav2vec2 - xls - r - 300m - german - de --dataset speech - recognition - community - v2/dev_data --config de --split validation --chunk_length_s 5.0 --stride_length_s 1.0

🔧 Technical Details

This model is based on the XLS - R architecture, which is a pre - trained model for speech representation learning. Fine - tuning on the German dataset of MOZILLA - FOUNDATION/COMMON_VOICE_7_0 helps the model adapt to German speech characteristics, such as pronunciation, intonation, and vocabulary.

📄 License

This model is licensed under the Apache 2.0 license.

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