Scribosermo: Fast Speech-to-Text models for German and other Languages
- URL: http://arxiv.org/abs/2110.07982v1
- Date: Fri, 15 Oct 2021 10:10:34 GMT
- Title: Scribosermo: Fast Speech-to-Text models for German and other Languages
- Authors: Daniel Bermuth, Alexander Poeppel, Wolfgang Reif
- Abstract summary: This paper presents Speech-to-Text models for German, as well as for Spanish and French with special features.
They are small and run in real-time on microcontrollers like a RaspberryPi.
Using a pretrained English model, they can be trained on consumer-grade hardware with a relatively small dataset.
- Score: 69.7571480246023
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: Recent Speech-to-Text models often require a large amount of hardware
resources and are mostly trained in English. This paper presents Speech-to-Text
models for German, as well as for Spanish and French with special features: (a)
They are small and run in real-time on microcontrollers like a RaspberryPi. (b)
Using a pretrained English model, they can be trained on consumer-grade
hardware with a relatively small dataset. (c) The models are competitive with
other solutions and outperform them in German. In this respect, the models
combine advantages of other approaches, which only include a subset of the
presented features. Furthermore, the paper provides a new library for handling
datasets, which is focused on easy extension with additional datasets and shows
an optimized way for transfer-learning new languages using a pretrained model
from another language with a similar alphabet.
Related papers
- Language Models on a Diet: Cost-Efficient Development of Encoders for Closely-Related Languages via Additional Pretraining [4.38070902806635]
We set up a benchmark for languages Croatian, Serbian, Bosnian and Montenegrin.
We show that comparable performance to dedicated from-scratch models can be obtained by additionally pretraining available multilingual models.
We also show that neighboring languages, in our case Slovenian, can be included in the additional pretraining with little to no loss in the performance of the final model.
arXiv Detail & Related papers (2024-04-08T11:55:44Z) - Multi-Task Contrastive Learning for 8192-Token Bilingual Text Embeddings [22.71166607645311]
We introduce a novel suite of state-of-the-art bilingual text embedding models.
These models are capable of processing lengthy text inputs with up to 8192 tokens.
We have significantly improved the model performance on STS tasks.
We have expanded the Massive Text Embedding Benchmark to include benchmarks for German and Spanish embedding models.
arXiv Detail & Related papers (2024-02-26T20:53:12Z) - ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot
Multilingual Information Retrieval [10.664434993386523]
Current approaches circumvent the lack of high-quality labeled data in non-English languages.
We present a novel modular dense retrieval model that learns from the rich data of a single high-resource language.
arXiv Detail & Related papers (2024-02-23T02:21:24Z) - Tik-to-Tok: Translating Language Models One Token at a Time: An
Embedding Initialization Strategy for Efficient Language Adaptation [19.624330093598996]
Training monolingual language models for low and mid-resource languages is made challenging by limited and often inadequate pretraining data.
By generalizing over a word translation dictionary encompassing both the source and target languages, we map tokens from the target tokenizer to semantically similar tokens from the source language tokenizer.
We conduct experiments to convert high-resource models to mid- and low-resource languages, namely Dutch and Frisian.
arXiv Detail & Related papers (2023-10-05T11:45:29Z) - Soft Language Clustering for Multilingual Model Pre-training [57.18058739931463]
We propose XLM-P, which contextually retrieves prompts as flexible guidance for encoding instances conditionally.
Our XLM-P enables (1) lightweight modeling of language-invariant and language-specific knowledge across languages, and (2) easy integration with other multilingual pre-training methods.
arXiv Detail & Related papers (2023-06-13T08:08:08Z) - Continual Learning in Multilingual NMT via Language-Specific Embeddings [92.91823064720232]
It consists in replacing the shared vocabulary with a small language-specific vocabulary and fine-tuning the new embeddings on the new language's parallel data.
Because the parameters of the original model are not modified, its performance on the initial languages does not degrade.
arXiv Detail & Related papers (2021-10-20T10:38:57Z) - Paraphrastic Representations at Scale [134.41025103489224]
We release trained models for English, Arabic, German, French, Spanish, Russian, Turkish, and Chinese languages.
We train these models on large amounts of data, achieving significantly improved performance from the original papers.
arXiv Detail & Related papers (2021-04-30T16:55:28Z) - UNKs Everywhere: Adapting Multilingual Language Models to New Scripts [103.79021395138423]
Massively multilingual language models such as multilingual BERT (mBERT) and XLM-R offer state-of-the-art cross-lingual transfer performance on a range of NLP tasks.
Due to their limited capacity and large differences in pretraining data, there is a profound performance gap between resource-rich and resource-poor target languages.
We propose novel data-efficient methods that enable quick and effective adaptation of pretrained multilingual models to such low-resource languages and unseen scripts.
arXiv Detail & Related papers (2020-12-31T11:37:28Z) - Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language
Model [58.27176041092891]
Recent research indicates that pretraining cross-lingual language models on large-scale unlabeled texts yields significant performance improvements.
We propose a novel unsupervised feature decomposition method that can automatically extract domain-specific features from the entangled pretrained cross-lingual representations.
Our proposed model leverages mutual information estimation to decompose the representations computed by a cross-lingual model into domain-invariant and domain-specific parts.
arXiv Detail & Related papers (2020-11-23T16:00:42Z) - ParsBERT: Transformer-based Model for Persian Language Understanding [0.7646713951724012]
This paper proposes a monolingual BERT for the Persian language (ParsBERT)
It shows its state-of-the-art performance compared to other architectures and multilingual models.
ParsBERT obtains higher scores in all datasets, including existing ones as well as composed ones.
arXiv Detail & Related papers (2020-05-26T05:05:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.