Transcribing a meeting, translating speech and answering a question about a sound recording are different tasks. A useful comparison starts with the required output rather than treating every audio model as a conversational assistant.
This guide compares documented capabilities. It does not report a new accuracy or latency benchmark.
Three distinct starting points
| Model or family | Documented role | Selection question |
|---|---|---|
| Whisper large-v3 / turbo | Speech recognition; translation support depends on the variant | Do you need a transcript or English translation? |
| SeamlessM4T v2 and related Seamless models | Multilingual speech/text translation, with separate expressive and streaming developments | Which input/output language pair and interaction mode are supported? |
| Qwen2-Audio-7B-Instruct | Audio and text input with textual responses | Do you need a transcript or an answer about the audio? |
OpenAI's turbo release note describes large-v3-turbo as an optimized Whisper variant and cautions about translation performance. It does not establish a Whisper v4 semantic-map API.
Meta's Seamless repository distinguishes SeamlessM4T v2, SeamlessExpressive and SeamlessStreaming. Do not attribute every capability of the family to one checkpoint.
The Qwen2-Audio model card describes audio analysis and speech instructions producing text. Accepting audio does not imply that the same model synthesizes a spoken response.
Evaluate the actual task
For a meeting archive, assemble consented clips containing the languages, microphones and overlapping speech expected in use. Produce reviewed reference transcripts. Score names, numbers and action items separately from an overall word-error rate: a single wrong invoice amount may matter more than several punctuation differences.
Speaker attribution is another measurement. A correct sentence assigned to the wrong participant can still make a meeting summary misleading. OpenAI's transcription guide documents a specialized diarization model and response format; speaker labeling should not be assumed for every transcription endpoint.
For translation, have competent speakers assess preserved meaning and omissions. For audio questions, include recordings where the evidence is insufficient and check whether the system invents an answer.
Design the surrounding pipeline
Keep source timestamps with derived text, expose corrections, and preserve a way to listen to the relevant segment. Measure upload, processing and playback separately if interactive latency matters.
A speech-to-text, language-model and text-to-speech pipeline remains a valid design when inspectable intermediate text is valuable. A native audio interface may offer different interaction properties, but neither architecture guarantees accuracy, emotion recognition or on-device privacy. Document where processing actually occurs and what recordings are retained.
Sardar Mukhshaf
Full-Stack & DevOps Engineer
I build scalable web applications, secure backend systems, and production-ready cloud infrastructure. My engineering focus spans interface design, API architecture, deployment pipelines, and observability. Follow for more deep dives into system architecture, performance engineering, and emerging technologies.
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