Search: speech-to-text - ai.jp.net

product #voice 🏛️ OfficialAnalyzed: Jan 16, 2026 10:45

Real-time AI Transcription: Unlocking Conversational Power!

Published:Jan 16, 2026 09:07

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1 min read

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Zenn OpenAI

Analysis

This article dives into the exciting possibilities of real-time transcription using OpenAI's Realtime API! It explores how to seamlessly convert live audio from push-to-talk systems into text, opening doors to innovative applications in communication and accessibility. This is a game-changer for interactive voice experiences!

Key Takeaways

•The article explores the technical details of real-time audio transcription.
•It leverages OpenAI's Realtime API.
•Focuses on streaming transcription for push-to-talk systems.

Reference

“The article focuses on utilizing the Realtime API to transcribe microphone input audio in real-time.”

Permalink Zenn OpenAI

product #voice 📝 BlogAnalyzed: Jan 6, 2026 07:24

Parakeet TDT: 30x Real-Time CPU Transcription Redefines Local STT

Published:Jan 5, 2026 19:49

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1 min read

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r/LocalLLaMA

Analysis

The claim of 30x real-time transcription on a CPU is significant, potentially democratizing access to high-performance STT. The compatibility with the OpenAI API and Open-WebUI further enhances its usability and integration potential, making it attractive for various applications. However, independent verification of the accuracy and robustness across all 25 languages is crucial.

Key Takeaways

•Parakeet TDT 0.6B V3 achieves 30x real-time transcription on an i7-12700KF CPU.
•The model supports 25 languages with automatic language detection.
•It is compatible with the OpenAI API and can be integrated into Open-WebUI.

Reference

“I’m now achieving 30x real-time speeds on an i7-12700KF. To put that in perspective: it processes one minute of audio in just 2 seconds.”

Permalink r/LocalLLaMA

Research #llm 📝 BlogAnalyzed: Dec 27, 2025 14:01

Gemini AI's Performance is Irrelevant, and Google Will Ruin It

Published:Dec 27, 2025 13:45

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1 min read

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r/artificial

Analysis

This article argues that Gemini's technical performance is less important than Google's historical track record of mismanaging and abandoning products. The author contends that tech reviewers often overlook Google's product lifecycle, which typically involves introduction, adoption, thriving, maintenance, and eventual abandonment. They cite Google's speech-to-text service as an example of a once-foundational technology that has been degraded due to cost-cutting measures, negatively impacting users who rely on it. The author also mentions Google Stadia as another example of a failed Google product, suggesting a pattern of mismanagement that will likely affect Gemini's long-term success.

Key Takeaways

•Google has a history of abandoning products, even those that are initially successful.
•Performance benchmarks alone are insufficient for evaluating the long-term viability of Google products.
•Cost-cutting measures can negatively impact the quality and accessibility of Google's core services.

Reference

“Anyone with an understanding of business and product management would get this, immediately. Yet a lot of these performance benchmarks and hype articles don't even mention this at all.”

Permalink r/artificial

Research Paper #Speech Recognition, Natural Language Processing, Machine Translation 🔬 ResearchAnalyzed: Jan 3, 2026 23:55

Rare Word Recognition and Translation Without Fine-Tuning

Published:Dec 26, 2025 06:51

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1 min read

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ArXiv

Analysis

This paper addresses a significant problem in speech-to-text systems: the difficulty of handling rare words. The proposed method offers a training-free alternative to fine-tuning, which is often costly and prone to issues like catastrophic forgetting. The use of task vectors and word-level arithmetic is a novel approach that promises scalability and reusability. The results, showing comparable or superior performance to fine-tuned models, are particularly noteworthy.

Key Takeaways

•Proposes a training-free method for rare word recognition and translation.
•Utilizes task vectors and word-level arithmetic for scalability and reusability.
•Achieves performance comparable to or better than fine-tuned models.
•Mitigates catastrophic forgetting, a common issue with fine-tuning.

Reference

“The proposed method matches or surpasses fine-tuned models on target words, improves general performance by about 5 BLEU, and mitigates catastrophic forgetting.”

Permalink ArXiv

AI #Healthcare 📝 BlogAnalyzed: Dec 24, 2025 08:22

Google Health AI Releases MedASR: A Medical Speech-to-Text Model

Published:Dec 24, 2025 04:10

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1 min read

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MarkTechPost

Analysis

This article announces the release of MedASR, a medical speech-to-text model developed by Google Health AI. The model, based on the Conformer architecture, is designed for clinical dictation and physician-patient conversations. The article highlights its potential to integrate into existing AI workflows. However, the provided content is very brief and lacks details about the model's performance, training data, or specific applications. Further information is needed to assess its true impact and value within the medical field. The open-weight nature is a positive aspect, potentially fostering wider adoption and research.

Key Takeaways

•Google Health AI released MedASR, a medical speech-to-text model.
•MedASR is based on the Conformer architecture.
•The model targets clinical dictation and physician-patient conversations.

Reference

“MedASR is a speech to text model based on the Conformer architecture and is pre”

Permalink MarkTechPost

Research #speech recognition 👥 CommunityAnalyzed: Dec 28, 2025 21:57

Can Fine-tuning ASR/STT Models Improve Performance on Severely Clipped Audio?

Published:Dec 23, 2025 04:29

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1 min read

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r/LanguageTechnology

Analysis

The article discusses the feasibility of fine-tuning Automatic Speech Recognition (ASR) or Speech-to-Text (STT) models to improve performance on heavily clipped audio data, a common problem in radio communications. The author is facing challenges with a company project involving metro train radio communications, where audio quality is poor due to clipping and domain-specific jargon. The core issue is the limited amount of verified data (1-2 hours) available for fine-tuning models like Whisper and Parakeet. The post raises a critical question about the practicality of the project given the data constraints and seeks advice on alternative methods. The problem highlights the challenges of applying state-of-the-art ASR models in real-world scenarios with imperfect audio.

Key Takeaways

•Fine-tuning ASR models on severely clipped audio is challenging due to limited data.
•The article highlights the practical difficulties of applying ASR in real-world noisy environments.
•Alternative methods, such as audio restoration techniques, might be necessary to improve performance.

Reference

“The audios our client have are borderline unintelligible to most people due to the many domain-specific jargons/callsigns and heavily clipped voices.”

Permalink r/LanguageTechnology

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 09:07

Simulstream: Open-Source Toolkit for Evaluation and Demonstration of Streaming Speech-to-Text Translation Systems

Published:Dec 19, 2025 14:48

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1 min read

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ArXiv

Analysis

This article introduces Simulstream, an open-source toolkit. The focus is on evaluating and demonstrating streaming speech-to-text translation systems. The toolkit's open-source nature promotes accessibility and collaboration within the research community.

Key Takeaways

•Simulstream is an open-source toolkit.
•It is designed for evaluating and demonstrating streaming speech-to-text translation systems.
•The open-source nature encourages collaboration and accessibility.

Reference

“”

Permalink ArXiv

Research #Translation 🔬 ResearchAnalyzed: Jan 10, 2026 13:40

MCAT: A New Approach to Multilingual Speech-to-Text Translation

Published:Dec 1, 2025 10:39

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1 min read

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ArXiv

Analysis

This research explores the use of Multilingual Large Language Models (MLLMs) to improve speech-to-text translation across 70 languages, a significant advancement in accessibility. The paper's contribution potentially streamlines communication in diverse linguistic contexts and could have broad implications for global information access.

Key Takeaways

•MCAT utilizes MLLMs for enhanced speech-to-text translation.
•The system supports translation across a wide range of 70 languages.
•The research aims to improve accessibility in multilingual communication.

Reference

“The research focuses on scaling Many-to-Many Speech-to-Text Translation with MLLMs to 70 languages.”

Permalink ArXiv

product #voice 📝 BlogAnalyzed: Jan 5, 2026 10:13

Choosing the Right AI Tool to Streamline Web Meeting Minutes: Top 5 Recommendations

Published:Aug 27, 2025 20:01

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1 min read

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AINOW

Analysis

The article targets a common pain point in business operations: the time-consuming task of creating meeting minutes. By focusing on AI-powered solutions, it addresses the potential for increased efficiency and productivity. However, a deeper analysis of the specific AI techniques used by these tools (e.g., speech-to-text accuracy, natural language understanding for summarization) would enhance its value.

Key Takeaways

•The article focuses on AI tools for automating meeting minutes.
•It aims to improve productivity by reducing time spent on transcription.
•The article provides recommendations for selecting suitable AI tools.

Reference

“"会議後の議事録作成に時間がかかりすぎて、生産性が低下している"”

Permalink AINOW

Technology #AI Speech Recognition 📝 BlogAnalyzed: Jan 3, 2026 06:37

Together AI Launches Speech-to-Text: High-Performance Whisper APIs

Published:Jul 10, 2025 00:00

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1 min read

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Together AI

Analysis

The article announces the launch of speech-to-text APIs by Together AI, leveraging the Whisper model. The focus is on high performance, suggesting improvements over existing solutions. The brevity of the article makes it difficult to assess the specifics of the performance claims or the target audience.

Key Takeaways

•Together AI has entered the speech-to-text market.
•The offering utilizes Whisper, a well-regarded speech recognition model.
•The APIs are marketed as high-performance.

Reference

“”

Permalink Together AI

Research #llm 📝 BlogAnalyzed: Dec 29, 2025 06:09

Building AI Voice Agents with Scott Stephenson - #707

Published:Oct 28, 2024 16:36

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1 min read

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Practical AI

Analysis

This article summarizes a podcast episode discussing the development of AI voice agents. It highlights the key components involved, including perception, understanding, and interaction. The discussion covers the use of multimodal LLMs, speech-to-text, and text-to-speech models. The episode also delves into the advantages and disadvantages of text-based approaches, the requirements for real-time voice interactions, and the potential of closed-loop, continuously improving agents. Finally, it mentions practical applications and a new agent toolkit from Deepgram. The focus is on the technical aspects of building and deploying AI voice agents.

Key Takeaways

•The episode explores the core components of AI voice agents: perception, understanding, and interaction.
•It discusses the role of multimodal LLMs, speech-to-text, and text-to-speech models in building these agents.
•The episode highlights the benefits and limitations of text-based approaches and the potential of real-time, continuously improving agents.

Reference

“The article doesn't contain a direct quote, but it discusses the topics covered in the podcast episode.”

Permalink Practical AI

AI Development #Voice AI, LLM, API 👥 CommunityAnalyzed: Jan 3, 2026 08:54

Retell AI: Conversational Speech API for LLMs

Published:Feb 21, 2024 13:18

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1 min read

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Hacker News

Analysis

Retell AI offers an API to simplify the development of natural-sounding voice AI applications. The core problem they address is the complexity of building conversational voice interfaces beyond basic ASR, LLM, and TTS integration. They highlight the importance of handling nuances like latency, backchanneling, and interruptions, which are crucial for a good user experience. The company aims to abstract away these complexities, allowing developers to focus on their application's core functionality. The Hacker News post serves as a launch announcement, including a demo video and a link to their website.

Key Takeaways

•Retell AI provides an API to simplify building conversational voice AI.
•The API addresses complexities beyond basic ASR, LLM, and TTS integration.
•Focus is on handling nuances like latency and backchanneling for a better user experience.
•The company aims to allow developers to focus on their application's core functionality.

Reference

“Developers often underestimate what's required to build a good and natural-sounding conversational voice AI. Many simply stitch together ASR (speech-to-text), an LLM, and TTS (text-to-speech), and expect to get a great experience. It turns out it's not that simple.”

Permalink Hacker News

Research #llm 👥 CommunityAnalyzed: Jan 4, 2026 09:38

Jarvis: A Voice Virtual Assistant in Python (OpenAI, ElevenLabs, Deepgram)

Published:Dec 18, 2023 13:27

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1 min read

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Hacker News

Analysis

This article announces the creation of a voice-based virtual assistant named Jarvis, built using Python and integrating services from OpenAI, ElevenLabs, and Deepgram. The focus is on the technical implementation and the use of various AI services for voice interaction. The article likely highlights the capabilities of the assistant, such as voice recognition, text-to-speech, and natural language understanding. The use of OpenAI suggests the assistant leverages LLMs for its core functionality.

Key Takeaways

•Demonstrates the practical application of LLMs and other AI services in building a voice assistant.
•Highlights the integration of different AI tools for a complete voice interaction experience.
•Provides insights into the technical aspects of developing a voice-based application using Python.

Reference

“The article likely details the specific roles of OpenAI (likely for LLM), ElevenLabs (likely for text-to-speech), and Deepgram (likely for speech-to-text).”

Permalink Hacker News

Research #ASR 👥 CommunityAnalyzed: Jan 10, 2026 15:56

OpenAI Unveils Whisper v3: Advancing Open Source Speech Recognition

Published:Nov 6, 2023 18:50

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1 min read

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Hacker News

Analysis

The release of Whisper v3 demonstrates continued progress in open-source Automatic Speech Recognition (ASR). This development could accelerate innovation and accessibility in speech-to-text technologies.

Key Takeaways

•Whisper v3 represents a significant advancement in open-source ASR.
•This release could foster broader adoption and development of speech recognition technologies.
•The open-source nature promotes community contributions and collaborative improvements.

Reference

“OpenAI releases Whisper v3, new generation open source ASR model”

Permalink Hacker News

AI News #Speech Recognition 👥 CommunityAnalyzed: Jan 3, 2026 16:01

OpenAI Whisper V2 Launch Analysis

Published:Dec 6, 2022 18:24

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1 min read

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Hacker News

Analysis

The article highlights the quiet release of OpenAI's Whisper V2 through a GitHub commit. This suggests a potentially significant update to the speech-to-text model, warranting further investigation into the improvements and implications of the new version. The 'quiet' launch implies a less formal announcement, possibly targeting developers and early adopters.

Key Takeaways

•Whisper V2 was released via a GitHub commit, indicating a potentially significant update.
•The 'quiet' launch suggests a focus on developers and early adopters.
•Further investigation is needed to understand the improvements and implications of V2.

Reference

“N/A - The article is a summary, not a direct quote.”

Permalink Hacker News

Product #Transcription 👥 CommunityAnalyzed: Jan 10, 2026 16:25

Real-time Audio Transcription with OpenAI's Whisper: A New Buzz

Published:Oct 20, 2022 18:33

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1 min read

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Hacker News

Analysis

The article highlights the use of OpenAI's Whisper model for real-time audio transcription directly from microphones, signaling a potential shift in accessibility for transcription services. This buzz could drive further innovation and competition within the speech-to-text landscape.

Key Takeaways

•OpenAI Whisper enables real-time transcription from microphones.
•This could have implications for accessibility and note-taking.
•The article is a news piece announcing this capability.

Reference

“Transcribing audio from your microphones in real-time using OpenAI's Whisper.”

Permalink Hacker News

Research #llm 📝 BlogAnalyzed: Dec 29, 2025 09:36

Boosting Wav2Vec2 with n-grams in 🤗 Transformers

Published:Jan 12, 2022 00:00

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1 min read

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Hugging Face

Analysis

This article likely discusses a method to improve the performance of the Wav2Vec2 model, a popular speech recognition model, by incorporating n-grams. N-grams, sequences of n words, are used to model word dependencies and improve the accuracy of speech-to-text tasks. The use of the Hugging Face Transformers library suggests the implementation is accessible and potentially easy to integrate. The article probably details the technical aspects of the implementation, including how n-grams are integrated into the Wav2Vec2 architecture and the performance gains achieved.

Key Takeaways

•Wav2Vec2 performance is improved.
•N-grams are used to model word dependencies.
•Implementation is likely facilitated by the Hugging Face Transformers library.

Reference

“The article likely includes a quote from a researcher or developer involved in the project, possibly highlighting the benefits of using n-grams or the ease of implementation with the Transformers library.”

Permalink Hugging Face

Real-time AI Transcription: Unlocking Conversational Power!

Analysis

Key Takeaways

Parakeet TDT: 30x Real-Time CPU Transcription Redefines Local STT

Analysis

Key Takeaways

Gemini AI's Performance is Irrelevant, and Google Will Ruin It

Analysis

Key Takeaways

Rare Word Recognition and Translation Without Fine-Tuning

Analysis

Key Takeaways

Google Health AI Releases MedASR: A Medical Speech-to-Text Model

Analysis

Key Takeaways

Can Fine-tuning ASR/STT Models Improve Performance on Severely Clipped Audio?

Analysis

Key Takeaways

Simulstream: Open-Source Toolkit for Evaluation and Demonstration of Streaming Speech-to-Text Translation Systems

Analysis

Key Takeaways

MCAT: A New Approach to Multilingual Speech-to-Text Translation

Analysis

Key Takeaways

Choosing the Right AI Tool to Streamline Web Meeting Minutes: Top 5 Recommendations

Analysis

Key Takeaways

Together AI Launches Speech-to-Text: High-Performance Whisper APIs

Analysis

Key Takeaways

Building AI Voice Agents with Scott Stephenson - #707

Analysis

Key Takeaways

Retell AI: Conversational Speech API for LLMs

Analysis

Key Takeaways

Jarvis: A Voice Virtual Assistant in Python (OpenAI, ElevenLabs, Deepgram)

Analysis

Key Takeaways

OpenAI Unveils Whisper v3: Advancing Open Source Speech Recognition

Analysis

Key Takeaways

OpenAI Whisper V2 Launch Analysis

Analysis

Key Takeaways

Real-time Audio Transcription with OpenAI's Whisper: A New Buzz

Analysis

Key Takeaways

Boosting Wav2Vec2 with n-grams in 🤗 Transformers

Analysis

Key Takeaways

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