Tag

#speech-recognition

Every item tagged speech-recognition, newest first.

8 items

IndicContextEval: A Benchmark for Evaluating Context Utilisation in Audio Large Language Models Across 8 Indic Languages

Researchers introduce IndicContextEval, a 56-hour benchmark evaluating how well audio large language models utilise contextual inputs across 8 Indian languages. The benchmark tests models' ability to incorporate domain descriptions and entity lists into speech recognition. This work aims to assess whether models truly leverage context or rely on pre-trained knowledge. You can use this benchmark to develop and evaluate models that better understand contextual cues in multilingual speech.

Key takeaways

IndicContextEval is a 56-hour multilingual benchmark.
Evaluates context utilisation in audio LLMs across 8 Indian languages.
Tests models' ability to incorporate domain descriptions and entity lists.

aarXiv#multilingual-llms #speech-recognition #benchmarks

research19h

Adaptive Speech-to-Spike Encoding for Spiking Neural Networks

Researchers propose an adaptive speech-to-spike encoding method for Spiking Neural Networks (SNNs). The approach uses a learnable residual encoder trained end-to-end with an R-LIF backbone. This adaptive encoding improves neuromorphic speech processing by reducing the mismatch between continuous acoustic signals and discrete SNN processing. You can apply this method to enhance speech recognition systems.

Key takeaways

Learnable residual encoder is jointly trained with R-LIF backbone.
Adaptive encoding reduces mismatch between acoustic signals and SNNs.
Improves neuromorphic speech processing accuracy.

aarXiv#spiking-neural-networks #neuromorphic-computing #speech-recognition

researchNov 21

Open ASR Leaderboard: Trends and Insights with New Multilingual & Long-Form Tracks

The Open ASR Leaderboard now includes multilingual and long-form speech recognition tracks, expanding its scope beyond English-only short-form transcription. This update enables more comprehensive evaluation of automatic speech recognition systems across diverse languages and audio formats. You can explore the refreshed leaderboard and dataset to assess ASR model performance in real-world scenarios. The leaderboard's growth reflects increasing demand for robust, multilingual ASR capabilities.

Key takeaways

Open ASR Leaderboard adds multilingual and long-form tracks.
Expanded scope enables more comprehensive ASR system evaluation.
Leaderboard now reflects growing demand for multilingual ASR.

HHugging Face Blog#open-source #speech-recognition #multilingual

toolsMay 1

Powerful ASR + diarization + speculative decoding with Hugging Face Inference Endpoints

Hugging Face has launched Inference Endpoints for automatic speech recognition (ASR) and diarization, enabling developers to deploy accurate, scalable speech-to-text solutions. The service supports real-time and batch processing, with optimized performance via speculative decoding. This allows builders to integrate ASR and diarization capabilities into their applications without managing complex infrastructure.

Key takeaways

Hugging Face offers ASR and diarization via Inference Endpoints.
Supports real-time and batch processing with optimized performance.
Enables scalable, accurate speech-to-text solutions for developers.

HHugging Face Blog#speech-recognition #inference #api

researchDec 20

Speculative Decoding for 2x Faster Whisper Inference

Hugging Face researchers implemented speculative decoding for Whisper, reducing inference time by 2x. This method generates multiple candidate transcriptions in parallel and selects the most likely one, improving efficiency without sacrificing accuracy. You can integrate this approach into your Whisper-based applications for faster performance. The technique is particularly useful for real-time transcription tasks where speed is crucial.

Key takeaways

Speculative decoding cuts Whisper inference time in half.
Method generates multiple transcription candidates in parallel.
Improves efficiency without losing accuracy.

HHugging Face Blog#real-time #speech-recognition #optimization

modelsJun 19

Fine-Tune MMS Adapter Models for low-resource ASR

Hugging Face released fine-tune MMS Adapter models for low-resource Automatic Speech Recognition (ASR). These models enable efficient adaptation to new languages with limited data. You can deploy them for ASR tasks in resource-constrained environments. The models are available on the Hugging Face Hub.

Key takeaways

Fine-tune MMS Adapter models for low-resource ASR tasks.
Efficient adaptation with limited data.
Deploy on Hugging Face Hub for ASR tasks.

HHugging Face Blog#fine-tuning #low-resource #speech-recognition

toolsJun 2

AI Speech Recognition in Unity

Hugging Face and Unity have collaborated to integrate AI speech recognition into Unity applications. This integration enables developers to add speech-to-text capabilities to their Unity projects. You can leverage pre-trained models for speech recognition. The integration aims to enhance user experience and interaction in Unity applications.

Key takeaways

Integration of AI speech recognition in Unity applications.
Enables developers to add speech-to-text capabilities.
Uses pre-trained models for speech recognition.

HHugging Face Blog#speech-recognition #unity #ai-integration

modelsJan 12

Boosting Wav2Vec2 with n-grams in 🤗 Transformers

The Hugging Face Transformers library now supports n-gram features for Wav2Vec2 models, enhancing their ability to capture local patterns in audio data. This update allows for more flexible and effective use of Wav2Vec2 in speech recognition and other audio processing tasks. You can leverage this feature to improve model performance in applications such as voice assistants and transcription services. The addition of n-gram support expands the toolkit for builders working with speech AI.

Key takeaways

Hugging Face Transformers adds n-gram support for Wav2Vec2.
Enables capturing local patterns in audio data.
Improves flexibility in speech recognition and audio processing tasks.

HHugging Face Blog#speech-recognition #transformers #audio-processing