Meetings and conversations
Transcribe meetings and combine the text with an LLM to prepare summaries. Retain recording references to check relevant passages.
Use cases / Audio and voice
Audio transcription
Transcribe audio with AI and make every conversation useful.
Turn meetings, calls, interviews and recordings into text you can search, summarize and use. Integrate speech recognition to connect audio content with the rest of your application.
With QDivZero, you can deploy ASR models and connect audio transcription through an API. Choose for the languages and conditions of your recordings, then combine the output with language models or semantic search.
Make spoken content usable
Bring spoken information into your applications.
Turn recordings into text you can query and reuse. Prepare content for search, analysis or editing.
Customer support calls
Turn calls into searchable content and classify reasons for contact. Link each transcript to the record your application uses.
Interviews and recordings
Prepare editable text from voice recordings. Compare models using your recordings’ languages, accents and conditions.
Videos, podcasts, and subtitles
Make speech in videos and podcasts searchable. For synchronised subtitles, use timestamps or add an alignment stage.
Build it with QDivZero
From a recording to text you can reuse.
Send recordings to a compatible ASR model and receive text for your application. If you need speakers or synchronised subtitles, check those outputs or add the required stages.
Your application flow
Recording
Prepare a compatible file or audio input.
Speech recognition
The ASR model converts spoken content into text.
Transcript
Receive text and available timestamps.
Application
Connect text to search, summaries, or analysis.
Open-weight / Hugging Face
You can start with…
Compare ASR models with your accents, noise levels and recording lengths. Qwen3-ASR and Whisper are starting points for accuracy, language coverage and capacity; check timestamps and other outputs supported by the runtime. A transcript can look broadly correct while missing important domain terms. Use known samples to compare those words alongside overall quality and the timing outputs your product requires.
Speech recognition
Qwen3-ASR-1.7B
Convert speech and audio recordings into text.
View model on Hugging FaceSpeech recognition
Qwen3-ASR-0.6B
A smaller alternative for comparing transcription efficiency and quality.
View model on Hugging FaceTranscription
Whisper Large V3 Turbo
A multilingual transcription option based on Whisper.
View model on Hugging FaceAudio transcription
Audio transcription: frequently asked questions
How do I transcribe audio to text with AI?
Use an automatic speech recognition model, or ASR, to convert a recording to text. Send compatible audio and receive the transcript. Deploy these models on QDivZero and connect them to your application through an API.
Which open-weight models can transcribe audio?
Start with Qwen3-ASR or Whisper Large V3 Turbo. Compare versions for your language, audio quality, and capacity needs. Also check formats and transcription options supported by the runtime you intend to deploy.
Can I transcribe meetings, interviews, and calls in different languages?
Yes, with a model supporting the required languages and inputs compatible with your recordings. Evaluate accents, noise, and real audio conditions. Your application can reuse the text for information queries or content processing.
How do I generate automatic subtitles for videos or podcasts?
Extract audio and use ASR to obtain a transcript. Synchronized subtitles require timestamps or an additional alignment step. Check model outputs and prepare the subtitle file in your application.
Can I summarize and search information in a transcript?
Yes. Send transcripts to an LLM for summaries or data extraction. Index text with embeddings for semantic search and RAG. This connects meeting and call content with the rest of your application knowledge.
How do I integrate a speech-to-text API into my product?
Deploy a compatible ASR model on QDivZero and connect your application to its endpoint. Prepare audio files or inputs according to the runtime and use returned text in your workflow. Size capacity for recording duration and volume.
What is the difference between transcription and speaker diarization?
Transcription recovers the words in a recording. Diarization identifies turns by different speakers. If you need to know who speaks when, check whether the model and runtime provide that output or add a specialized stage to your transcription process.
How much does AI audio transcription cost?
It depends on recording duration, the model and how many files you need to process concurrently. Audio conditions and additional stages such as alignment or diarization also affect workload. Check QDivZero pricing and use your recordings to size capacity and estimate processing time.
Ready to turn conversations into useful information?
Integrate audio transcription into your products and processes. Deploy a compatible ASR model in QDivZero and connect transcripts with search, summaries or information extraction.