feat(parse): implement audio resource parser with Whisper transcription by mvanhorn · Pull Request #707 · volcengine/OpenViking

mvanhorn · 2026-03-17T14:17:05Z

Summary

Replace the audio parser stub at openviking/parse/parsers/media/audio.py with a working implementation
Extract audio metadata (duration, sample rate, channels, bitrate) via mutagen (optional dependency)
Transcribe speech to text via Whisper API through the existing OpenAI client, with timestamped segment output
Build structured ResourceNode tree with L0 abstract, L1 overview, and L2 full transcript with timestamps
Graceful degradation: produces metadata-only ResourceNode when Whisper is unavailable or mutagen is not installed

This addresses the audio parser stub and aligns with the Q2 multimodal roadmap mentioned in #372. The TODO comments at audio.py:172 ("Integrate with actual ASR API (Whisper, etc.)") and audio.py:190 ("Integrate with ASR API") are resolved by this implementation.

Changes

File	Change
`openviking/parse/parsers/media/audio.py`	Replace stub with full implementation (~540 lines)
`openviking/prompts/templates/parsing/audio_summary.yaml`	New prompt template for transcript summarization
`tests/unit/parse/test_audio_parser.py`	Unit tests with mocked Whisper API and mutagen
`pyproject.toml`	Add `mutagen>=1.47.0` as optional `[audio]` dependency

Design

Follows BaseParser interface and ImageParser patterns exactly
Uses lazy imports for mutagen (graceful fallback if not installed)
Uses openai.AsyncOpenAI for Whisper API calls, reusing the existing provider config

ResourceNode tree structure:

ROOT (audio metadata + L0 abstract)
+-- segment_001 (0:00-0:30, transcript text)
+-- segment_002 (0:30-1:15, transcript text)
+-- ...

Evidence

Source	Evidence	Engagement
#372	Multimodal resource parsing requested, MaojiaSheng confirmed Q2 plans	Active
audio.py:172	TODO: "Integrate with actual ASR API (Whisper, etc.)"	In-code spec
#196	MaojiaSheng authored media parser updates - established the stub structure	Merged
TikTok	"OpenViking: Context DB for AI Agent" - growing user awareness	11,644 views

Test plan

Unit tests pass: pytest tests/unit/parse/test_audio_parser.py -v
Timestamp formatting covers edge cases (0s, minutes, hours)
Mutagen metadata extraction tested with mocked library
Graceful fallback when mutagen is not installed
Graceful fallback when Whisper API is unavailable
Magic bytes validation for MP3, WAV, OGG, FLAC, AAC formats
ResourceNode tree structure verified with mocked transcript segments
Lint passes: ruff check openviking/parse/parsers/media/audio.py

Submitted via osc-newfeature

Replace the audio parser stub with a working implementation that: - Extracts metadata (duration, sample rate, channels, bitrate) via mutagen - Transcribes speech via Whisper API with timestamped segments - Builds structured ResourceNode tree with L0/L1/L2 content tiers - Falls back to metadata-only output when Whisper is unavailable - Adds mutagen as optional dependency under [audio] extra - Adds audio_summary prompt template for semantic indexing - Includes unit tests with mocked Whisper API and mutagen

qin-ctx

Review Summary

The core audio parser implementation is well-structured with good graceful degradation design. However, there are critical issues with the Whisper API integration that make transcription non-functional under default configuration.

Blocking Issues

Default model name incompatible with OpenAI Whisper API
No base_url support for custom Whisper endpoints
audio_summary.yaml prompt template added but never used

Non-blocking

Duplicate boilerplate across transcription methods
~13 files have unrelated ruff formatting changes mixed into this feature PR — consider splitting formatting into a separate PR for easier review and bisect

Note

This PR currently has merge conflicts that need to be resolved.

qin-ctx · 2026-03-18T09:27:31Z

openviking/parse/parsers/media/audio.py

+            audio_file.name = f"audio{ext}"
+
+            response = await client.audio.transcriptions.create(
+                model=model or "whisper-1",


[Bug] (blocking) The default transcription_model in AudioConfig is "whisper-large-v3", which is a HuggingFace/local model name. OpenAI's Whisper API only accepts "whisper-1". Since model is "whisper-large-v3" (truthy), model or "whisper-1" evaluates to "whisper-large-v3", and the API call will fail with an invalid model error.

The error is silently caught by the except block at line 344, so users get metadata-only output without any clear indication that transcription is broken.

Suggested fix: either change the AudioConfig default to "whisper-1", or add model name mapping logic for different Whisper providers.

qin-ctx · 2026-03-18T09:27:32Z

openviking/parse/parsers/media/audio.py

+            import openai
+
+            client = openai.AsyncOpenAI(
+                api_key=config.llm.api_key if hasattr(config, "llm") else None,


[Bug] (blocking) openai.AsyncOpenAI is created without a base_url parameter, so it always connects to OpenAI's default endpoint. The project's ProviderConfig supports custom API endpoints via api_base, but this code ignores it entirely. Users with custom Whisper deployments (Azure OpenAI, local Whisper server, etc.) cannot use transcription.

[Suggestion] (non-blocking) This direct client creation is also inconsistent with the project's patterns — ImageParser uses get_openviking_config().vlm for model calls, integrating with the provider infrastructure. Consider either reusing an existing provider client or at least reading api_base from the config to construct the client.

qin-ctx · 2026-03-18T09:27:32Z

openviking/prompts/templates/parsing/audio_summary.yaml

@@ -0,0 +1,44 @@
+metadata:


[Design] (blocking) This prompt template (parsing.audio_summary) is defined but never referenced anywhere in the code. For comparison, ImageParser uses render_prompt("parsing.image_summary", ...) to generate semantic summaries via LLM, but AudioParser._generate_semantic_info() at audio.py:456 does simple string truncation instead of LLM-powered summarization.

Either integrate this template with render_prompt() in _generate_semantic_info (following the ImageParser pattern), or remove this file to avoid shipping dead code.

qin-ctx · 2026-03-18T09:27:32Z

openviking/parse/parsers/media/audio.py

-            Transcript with timestamps in markdown format, or None if not available
+            List of segment dicts with keys: start, end, text
+        """
+        try:


[Design] (non-blocking) _asr_transcribe_with_timestamps (lines 348-406) contains identical boilerplate to _asr_transcribe (lines 309-346): config loading, openai.AsyncOpenAI client creation, and BytesIO wrapping. Consider extracting shared helpers like _get_whisper_client() and _prepare_audio_file() to reduce duplication and make the API integration easier to update.

The audio parser feature is unrelated to memory health stats and belongs in its own PR (volcengine#707). Reverts audio.py to pre-rewrite state, removes the unused audio_summary.yaml template and audio parser tests. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

…cation - Change default transcription_model from "whisper-large-v3" (HuggingFace name) to "whisper-1" (OpenAI API compatible) - Add base_url support via ProviderConfig.api_base so custom Whisper deployments (Azure, local server) work correctly - Extract _get_whisper_client() and _prepare_audio_file() helpers to eliminate duplicate boilerplate between _asr_transcribe and _asr_transcribe_with_timestamps - Remove unused audio_summary.yaml template (will integrate with render_prompt in a follow-up if LLM-powered summarization is desired) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

mvanhorn · 2026-03-18T13:59:05Z

Addressed all feedback in a6a57ce:

Changed default transcription_model from "whisper-large-v3" to "whisper-1" (OpenAI API compatible)
Added base_url support via ProviderConfig.api_base so custom Whisper deployments work
Extracted _get_whisper_client() and _prepare_audio_file() helpers to eliminate the duplicate boilerplate
Removed the unused audio_summary.yaml template (can integrate with render_prompt in a follow-up if LLM-powered summarization is desired)

mvanhorn · 2026-03-18T22:07:12Z

Addressed all feedback in a6a57ce:

Default model: Changed from whisper-large-v3 (HuggingFace name) to whisper-1 (OpenAI API compatible). The model or "whisper-1" fallback now only triggers when model is explicitly None.
base_url support: _get_whisper_client() now reads api_base from ProviderConfig, matching how ImageParser uses get_openviking_client(). Custom Whisper deployments (Azure, local) work correctly.
Duplicate boilerplate: Extracted _get_whisper_client() and _prepare_audio_file() shared helpers. Both _asr_transcribe and _asr_transcribe_with_timestamps use them.
Unused template: Removed audio_summary.yaml. Will integrate with render_prompt() in a follow-up if LLM-powered summarization is desired.

github-project-automation bot added this to OpenViking project Mar 17, 2026

github-project-automation bot moved this to Backlog in OpenViking project Mar 17, 2026

style: format with ruff

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qin-ctx requested changes Mar 18, 2026

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mvanhorn mentioned this pull request Mar 18, 2026

feat(server): add memory health statistics API endpoints #706

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feat(parse): implement audio resource parser with Whisper transcription#707

feat(parse): implement audio resource parser with Whisper transcription#707
mvanhorn wants to merge 3 commits intovolcengine:mainfrom
mvanhorn:feat/audio-resource-parser

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mvanhorn commented Mar 17, 2026

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