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8 Commits

Author SHA1 Message Date
lebaudantoine 35951ba2a6 🔖(minor) bump release to 1.16.0 2026-05-13 22:30:32 +02:00
lebaudantoine 72184e1370 🩹(frontend) fix spacing regression in mobile control bar
Correct excessive spacing between action buttons in the mobile
control bar introduced by a recent layout change.
2026-05-13 20:15:32 +02:00
leo 1b4a8fbac2 🔧(agents) fix Docker setup
Fix two issues. 1: Missmatch between commands in dev and production in
Dockerfile, leading to unexpected behaviors. 2: Naming of
multi-user-transcriber -> multi-user-transcriber-dev for coherence.
2026-05-13 20:07:45 +02:00
lebaudantoine 1e2fad5444 ️(mail) revert mail upgrade due to unhandled breaking changes
Rollback the mail package upgrade after identifying multiple
breaking changes introduced in v5 that were not fully accounted
for.

Local testing initially missed the issue because the mail Docker
image had not been rebuilt automatically, causing broken emails to
go unnoticed.
2026-05-13 19:55:54 +02:00
leo 96f97ed2d0 (summary) improve speaker assignment
Speaker-to-participant assignment relie on WhisperX word timings, but
incorrect word durations in the output can lead to inaccurate overlap
scoring and wrong user attribution. Add a custom heuristic to trim
overly long word durations before computing assignments.
2026-05-12 16:58:07 +02:00
lebaudantoine 02d16cb55c ⬆️(addons) update dependencies 2026-05-12 16:26:16 +02:00
lebaudantoine 7268ff6777 ⬆️(mail) update dependencies 2026-05-12 16:26:16 +02:00
lebaudantoine cca5bc2186 ⬆️(frontend) update dependencies 2026-05-12 16:26:16 +02:00
25 changed files with 796 additions and 1280 deletions
+4 -3
View File
@@ -8,6 +8,8 @@ and this project adheres to
## [Unreleased]
## [1.16.0] - 2026-05-13
### Added
- 🔒️(backend) add validation of Room.configuration
@@ -19,10 +21,8 @@ and this project adheres to
- ♻️(summary) change tasks endpoint signature
- ⬆️(dependencies) update urllib3 to v2.7.0 [SECURITY]
### Changed
- 🧑‍💻(agents) use `uv` for package management
- ✨(summary) improve speaker-to-participant assignment
### Fixed
@@ -32,6 +32,7 @@ and this project adheres to
- ⬆️(mail) fix dependencies not having resolved or integrity field #1321
- 🐛(summary) complete webm support #1328
- 🐛(backend) add link to "Open" text in recording email
- 🩹(frontend) fix spacing regression in mobile control bar
## [1.15.0] - 2026-04-30
+2 -2
View File
@@ -109,7 +109,7 @@ build-frontend: ## build the frontend container
.PHONY: build-frontend
build-agents: ## build the multi-user-transcriber agent container
@$(COMPOSE) build multi-user-transcriber
@$(COMPOSE) build multi-user-transcriber-dev
.PHONY: build-agents
down: ## stop and remove containers, networks, images, and volumes
@@ -138,7 +138,7 @@ run-agents: ## start the multi-user-transcriber agent
.PHONY: run-agents
run-agent-multi-user-transcriber: ## start the LiveKit agents (multi users transcriber)
@$(COMPOSE) up --force-recreate -d multi-user-transcriber
@$(COMPOSE) up --force-recreate -d multi-user-transcriber-dev
.PHONY: run-agent-multi-user-transcriber
run-agent-metadata-collector: ## start the LiveKit agents (metadata collector)
+10 -11
View File
@@ -271,6 +271,16 @@ services:
- action: rebuild
path: ./src/agents
multi-user-transcriber-dev:
build:
context: ./src/agents
target: development
env_file:
- env.d/development/multi_user_transcriber
volumes:
- ./src/agents:/app
- /app/.venv
redis-summary:
image: redis
ports:
@@ -332,17 +342,6 @@ services:
- action: rebuild
path: ./src/summary
multi-user-transcriber:
build:
context: ./src/agents
target: development
command: ["python", "multi_user_transcriber.py", "dev"]
env_file:
- env.d/development/multi_user_transcriber
volumes:
- ./src/agents:/app
- /app/.venv
networks:
default:
resource-server:
+3 -15
View File
@@ -1,21 +1,9 @@
AWS_S3_ENDPOINT_URL=minio:9000
AWS_S3_ACCESS_KEY_ID=meet
AWS_S3_SECRET_ACCESS_KEY=password
LIVEKIT_URL=ws://livekit:7880
LIVEKIT_API_KEY=devkey
LIVEKIT_API_SECRET=secret
STT_PROVIDER=voxtral-vllm # voxtral-vllm, kyutai, deepgram
STT_PROVIDER=kyutai
ENABLE_SILERO_VAD=False
DEEPGRAM_API_KEY=your-deepgram-api-key
KYUTAI_STT_BASE_URL=url
KYUTAI_API_KEY=your-kyutai-api-key
VOXTRAL_VLLM_BASE_URL=wss://<host>/v1/realtime
VOXTRAL_VLLM_MODEL=voxtral-mini-4b-realtime-2602
VOXTRAL_VLLM_API_KEY=your-vllm-api-key
VOXTRAL_VLLM_TARGET_STREAMING_DELAY_MS=480
KYUTAI_STT_BASE_URL=
KYUTAI_API_KEY=
+12 -250
View File
@@ -19,7 +19,7 @@
"@types/office-runtime": "^1.0.35",
"acorn": "^8.11.3",
"babel-loader": "^9.1.3",
"copy-webpack-plugin": "^12.0.2",
"copy-webpack-plugin": "^14.0.0",
"eslint-plugin-office-addins": "^4.0.3",
"file-loader": "^6.2.0",
"html-loader": "^5.0.0",
@@ -4319,44 +4319,6 @@
"node": ">= 4.0.0"
}
},
"node_modules/@nodelib/fs.scandir": {
"version": "2.1.5",
"resolved": "https://registry.npmjs.org/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz",
"integrity": "sha512-vq24Bq3ym5HEQm2NKCr3yXDwjc7vTsEThRDnkp2DK9p1uqLR+DHurm/NOTo0KG7HYHU7eppKZj3MyqYuMBf62g==",
"dev": true,
"license": "MIT",
"dependencies": {
"@nodelib/fs.stat": "2.0.5",
"run-parallel": "^1.1.9"
},
"engines": {
"node": ">= 8"
}
},
"node_modules/@nodelib/fs.stat": {
"version": "2.0.5",
"resolved": "https://registry.npmjs.org/@nodelib/fs.stat/-/fs.stat-2.0.5.tgz",
"integrity": "sha512-RkhPPp2zrqDAQA/2jNhnztcPAlv64XdhIp7a7454A5ovI7Bukxgt7MX7udwAu3zg1DcpPU0rz3VV1SeaqvY4+A==",
"dev": true,
"license": "MIT",
"engines": {
"node": ">= 8"
}
},
"node_modules/@nodelib/fs.walk": {
"version": "1.2.8",
"resolved": "https://registry.npmjs.org/@nodelib/fs.walk/-/fs.walk-1.2.8.tgz",
"integrity": "sha512-oGB+UxlgWcgQkgwo8GcEGwemoTFt3FIO9ababBmaGwXIoBKZ+GTy0pP185beGg7Llih/NSHSV2XAs1lnznocSg==",
"dev": true,
"license": "MIT",
"dependencies": {
"@nodelib/fs.scandir": "2.1.5",
"fastq": "^1.6.0"
},
"engines": {
"node": ">= 8"
}
},
"node_modules/@pkgr/core": {
"version": "0.2.9",
"resolved": "https://registry.npmjs.org/@pkgr/core/-/core-0.2.9.tgz",
@@ -4370,19 +4332,6 @@
"url": "https://opencollective.com/pkgr"
}
},
"node_modules/@sindresorhus/merge-streams": {
"version": "2.3.0",
"resolved": "https://registry.npmjs.org/@sindresorhus/merge-streams/-/merge-streams-2.3.0.tgz",
"integrity": "sha512-LtoMMhxAlorcGhmFYI+LhPgbPZCkgP6ra1YL604EeF6U98pLlQ3iWIGMdWSC+vWmPBWBNgmDBAhnAobLROJmwg==",
"dev": true,
"license": "MIT",
"engines": {
"node": ">=18"
},
"funding": {
"url": "https://github.com/sponsors/sindresorhus"
}
},
"node_modules/@types/body-parser": {
"version": "1.19.6",
"resolved": "https://registry.npmjs.org/@types/body-parser/-/body-parser-1.19.6.tgz",
@@ -6686,21 +6635,20 @@
"license": "MIT"
},
"node_modules/copy-webpack-plugin": {
"version": "12.0.2",
"resolved": "https://registry.npmjs.org/copy-webpack-plugin/-/copy-webpack-plugin-12.0.2.tgz",
"integrity": "sha512-SNwdBeHyII+rWvee/bTnAYyO8vfVdcSTud4EIb6jcZ8inLeWucJE0DnxXQBjlQ5zlteuuvooGQy3LIyGxhvlOA==",
"version": "14.0.0",
"resolved": "https://registry.npmjs.org/copy-webpack-plugin/-/copy-webpack-plugin-14.0.0.tgz",
"integrity": "sha512-3JLW90aBGeaTLpM7mYQKpnVdgsUZRExY55giiZgLuX/xTQRUs1dOCwbBnWnvY6Q6rfZoXMNwzOQJCSZPppfqXA==",
"dev": true,
"license": "MIT",
"dependencies": {
"fast-glob": "^3.3.2",
"glob-parent": "^6.0.1",
"globby": "^14.0.0",
"normalize-path": "^3.0.0",
"schema-utils": "^4.2.0",
"serialize-javascript": "^6.0.2"
"serialize-javascript": "^7.0.3",
"tinyglobby": "^0.2.12"
},
"engines": {
"node": ">= 18.12.0"
"node": ">= 20.9.0"
},
"funding": {
"type": "opencollective",
@@ -8062,36 +8010,6 @@
"dev": true,
"license": "Apache-2.0"
},
"node_modules/fast-glob": {
"version": "3.3.3",
"resolved": "https://registry.npmjs.org/fast-glob/-/fast-glob-3.3.3.tgz",
"integrity": "sha512-7MptL8U0cqcFdzIzwOTHoilX9x5BrNqye7Z/LuC7kCMRio1EMSyqRK3BEAUD7sXRq4iT4AzTVuZdhgQ2TCvYLg==",
"dev": true,
"license": "MIT",
"dependencies": {
"@nodelib/fs.stat": "^2.0.2",
"@nodelib/fs.walk": "^1.2.3",
"glob-parent": "^5.1.2",
"merge2": "^1.3.0",
"micromatch": "^4.0.8"
},
"engines": {
"node": ">=8.6.0"
}
},
"node_modules/fast-glob/node_modules/glob-parent": {
"version": "5.1.2",
"resolved": "https://registry.npmjs.org/glob-parent/-/glob-parent-5.1.2.tgz",
"integrity": "sha512-AOIgSQCepiJYwP3ARnGx+5VnTu2HBYdzbGP45eLw1vr3zB3vZLeyed1sC9hnbcOc9/SrMyM5RPQrkGz4aS9Zow==",
"dev": true,
"license": "ISC",
"dependencies": {
"is-glob": "^4.0.1"
},
"engines": {
"node": ">= 6"
}
},
"node_modules/fast-json-stable-stringify": {
"version": "2.1.0",
"resolved": "https://registry.npmjs.org/fast-json-stable-stringify/-/fast-json-stable-stringify-2.1.0.tgz",
@@ -8180,16 +8098,6 @@
"node": ">= 4.9.1"
}
},
"node_modules/fastq": {
"version": "1.20.1",
"resolved": "https://registry.npmjs.org/fastq/-/fastq-1.20.1.tgz",
"integrity": "sha512-GGToxJ/w1x32s/D2EKND7kTil4n8OVk/9mycTc4VDza13lOvpUZTGX3mFSCtV9ksdGBVzvsyAVLM6mHFThxXxw==",
"dev": true,
"license": "ISC",
"dependencies": {
"reusify": "^1.0.4"
}
},
"node_modules/faye-websocket": {
"version": "0.11.4",
"resolved": "https://registry.npmjs.org/faye-websocket/-/faye-websocket-0.11.4.tgz",
@@ -8758,37 +8666,6 @@
"url": "https://github.com/sponsors/ljharb"
}
},
"node_modules/globby": {
"version": "14.1.0",
"resolved": "https://registry.npmjs.org/globby/-/globby-14.1.0.tgz",
"integrity": "sha512-0Ia46fDOaT7k4og1PDW4YbodWWr3scS2vAr2lTbsplOt2WkKp0vQbkI9wKis/T5LV/dqPjO3bpS/z6GTJB82LA==",
"dev": true,
"license": "MIT",
"dependencies": {
"@sindresorhus/merge-streams": "^2.1.0",
"fast-glob": "^3.3.3",
"ignore": "^7.0.3",
"path-type": "^6.0.0",
"slash": "^5.1.0",
"unicorn-magic": "^0.3.0"
},
"engines": {
"node": ">=18"
},
"funding": {
"url": "https://github.com/sponsors/sindresorhus"
}
},
"node_modules/globby/node_modules/ignore": {
"version": "7.0.5",
"resolved": "https://registry.npmjs.org/ignore/-/ignore-7.0.5.tgz",
"integrity": "sha512-Hs59xBNfUIunMFgWAbGX5cq6893IbWg4KnrjbYwX3tx0ztorVgTDA6B2sxf8ejHJ4wz8BqGUMYlnzNBer5NvGg==",
"dev": true,
"license": "MIT",
"engines": {
"node": ">= 4"
}
},
"node_modules/gopd": {
"version": "1.2.0",
"resolved": "https://registry.npmjs.org/gopd/-/gopd-1.2.0.tgz",
@@ -11044,16 +10921,6 @@
"dev": true,
"license": "MIT"
},
"node_modules/merge2": {
"version": "1.4.1",
"resolved": "https://registry.npmjs.org/merge2/-/merge2-1.4.1.tgz",
"integrity": "sha512-8q7VEgMJW4J8tcfVPy8g09NcQwZdbwFEqhe/WZkoIzjn/3TGDwtOCYtXGxA3O8tPzpczCCDgv+P2P5y00ZJOOg==",
"dev": true,
"license": "MIT",
"engines": {
"node": ">= 8"
}
},
"node_modules/methods": {
"version": "1.1.2",
"resolved": "https://registry.npmjs.org/methods/-/methods-1.1.2.tgz",
@@ -12638,19 +12505,6 @@
"dev": true,
"license": "MIT"
},
"node_modules/path-type": {
"version": "6.0.0",
"resolved": "https://registry.npmjs.org/path-type/-/path-type-6.0.0.tgz",
"integrity": "sha512-Vj7sf++t5pBD637NSfkxpHSMfWaeig5+DKWLhcqIYx6mWQz5hdJTGDVMQiJcw1ZYkhs7AazKDGpRVji1LJCZUQ==",
"dev": true,
"license": "MIT",
"engines": {
"node": ">=18"
},
"funding": {
"url": "https://github.com/sponsors/sindresorhus"
}
},
"node_modules/pathval": {
"version": "1.1.1",
"resolved": "https://registry.npmjs.org/pathval/-/pathval-1.1.1.tgz",
@@ -12985,37 +12839,6 @@
"url": "https://github.com/sponsors/ljharb"
}
},
"node_modules/queue-microtask": {
"version": "1.2.3",
"resolved": "https://registry.npmjs.org/queue-microtask/-/queue-microtask-1.2.3.tgz",
"integrity": "sha512-NuaNSa6flKT5JaSYQzJok04JzTL1CA6aGhv5rfLW3PgqA+M2ChpZQnAC8h8i4ZFkBS8X5RqkDBHA7r4hej3K9A==",
"dev": true,
"funding": [
{
"type": "github",
"url": "https://github.com/sponsors/feross"
},
{
"type": "patreon",
"url": "https://www.patreon.com/feross"
},
{
"type": "consulting",
"url": "https://feross.org/support"
}
],
"license": "MIT"
},
"node_modules/randombytes": {
"version": "2.1.0",
"resolved": "https://registry.npmjs.org/randombytes/-/randombytes-2.1.0.tgz",
"integrity": "sha512-vYl3iOX+4CKUWuxGi9Ukhie6fsqXqS9FE2Zaic4tNFD2N2QQaXOMFbuKK4QmDHC0JO6B1Zp41J0LpT0oR68amQ==",
"dev": true,
"license": "MIT",
"dependencies": {
"safe-buffer": "^5.1.0"
}
},
"node_modules/range-parser": {
"version": "1.2.1",
"resolved": "https://registry.npmjs.org/range-parser/-/range-parser-1.2.1.tgz",
@@ -13502,17 +13325,6 @@
"node": ">= 4"
}
},
"node_modules/reusify": {
"version": "1.1.0",
"resolved": "https://registry.npmjs.org/reusify/-/reusify-1.1.0.tgz",
"integrity": "sha512-g6QUff04oZpHs0eG5p83rFLhHeV00ug/Yf9nZM6fLeUrPguBTkTQOdpAWWspMh55TZfVQDPaN3NQJfbVRAxdIw==",
"dev": true,
"license": "MIT",
"engines": {
"iojs": ">=1.0.0",
"node": ">=0.10.0"
}
},
"node_modules/rfdc": {
"version": "1.4.1",
"resolved": "https://registry.npmjs.org/rfdc/-/rfdc-1.4.1.tgz",
@@ -13545,30 +13357,6 @@
"node": ">=0.12.0"
}
},
"node_modules/run-parallel": {
"version": "1.2.0",
"resolved": "https://registry.npmjs.org/run-parallel/-/run-parallel-1.2.0.tgz",
"integrity": "sha512-5l4VyZR86LZ/lDxZTR6jqL8AFE2S0IFLMP26AbjsLVADxHdhB/c0GUsH+y39UfCi3dzz8OlQuPmnaJOMoDHQBA==",
"dev": true,
"funding": [
{
"type": "github",
"url": "https://github.com/sponsors/feross"
},
{
"type": "patreon",
"url": "https://www.patreon.com/feross"
},
{
"type": "consulting",
"url": "https://feross.org/support"
}
],
"license": "MIT",
"dependencies": {
"queue-microtask": "^1.2.2"
}
},
"node_modules/rxjs": {
"version": "7.8.2",
"resolved": "https://registry.npmjs.org/rxjs/-/rxjs-7.8.2.tgz",
@@ -13842,13 +13630,13 @@
}
},
"node_modules/serialize-javascript": {
"version": "6.0.2",
"resolved": "https://registry.npmjs.org/serialize-javascript/-/serialize-javascript-6.0.2.tgz",
"integrity": "sha512-Saa1xPByTTq2gdeFZYLLo+RFE35NHZkAbqZeWNd3BpzppeVisAqpDjcp8dyf6uIvEqJRd46jemmyA4iFIeVk8g==",
"version": "7.0.5",
"resolved": "https://registry.npmjs.org/serialize-javascript/-/serialize-javascript-7.0.5.tgz",
"integrity": "sha512-F4LcB0UqUl1zErq+1nYEEzSHJnIwb3AF2XWB94b+afhrekOUijwooAYqFyRbjYkm2PAKBabx6oYv/xDxNi8IBw==",
"dev": true,
"license": "BSD-3-Clause",
"dependencies": {
"randombytes": "^2.1.0"
"engines": {
"node": ">=20.0.0"
}
},
"node_modules/serve-index": {
@@ -14260,19 +14048,6 @@
"simple-concat": "^1.0.0"
}
},
"node_modules/slash": {
"version": "5.1.0",
"resolved": "https://registry.npmjs.org/slash/-/slash-5.1.0.tgz",
"integrity": "sha512-ZA6oR3T/pEyuqwMgAKT0/hAv8oAXckzbkmR0UkUosQ+Mc4RxGoJkRmwHgHufaenlyAgE1Mxgpdcrf75y6XcnDg==",
"dev": true,
"license": "MIT",
"engines": {
"node": ">=14.16"
},
"funding": {
"url": "https://github.com/sponsors/sindresorhus"
}
},
"node_modules/sockjs": {
"version": "0.3.24",
"resolved": "https://registry.npmjs.org/sockjs/-/sockjs-0.3.24.tgz",
@@ -15311,19 +15086,6 @@
"node": ">=4"
}
},
"node_modules/unicorn-magic": {
"version": "0.3.0",
"resolved": "https://registry.npmjs.org/unicorn-magic/-/unicorn-magic-0.3.0.tgz",
"integrity": "sha512-+QBBXBCvifc56fsbuxZQ6Sic3wqqc3WWaqxs58gvJrcOuN83HGTCwz3oS5phzU9LthRNE9VrJCFCLUgHeeFnfA==",
"dev": true,
"license": "MIT",
"engines": {
"node": ">=18"
},
"funding": {
"url": "https://github.com/sponsors/sindresorhus"
}
},
"node_modules/universalify": {
"version": "2.0.1",
"resolved": "https://registry.npmjs.org/universalify/-/universalify-2.0.1.tgz",
+1 -1
View File
@@ -36,7 +36,7 @@
"@types/office-runtime": "^1.0.35",
"acorn": "^8.11.3",
"babel-loader": "^9.1.3",
"copy-webpack-plugin": "^12.0.2",
"copy-webpack-plugin": "^14.0.0",
"eslint-plugin-office-addins": "^4.0.3",
"file-loader": "^6.2.0",
"html-loader": "^5.0.0",
+1 -1
View File
@@ -49,7 +49,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
ENV PATH="/app/.venv/bin:$PATH"
CMD ["python", "metadata_collector.py", "dev"]
CMD ["python", "multi_user_transcriber.py", "dev"]
# ---- Production image ----
-5
View File
@@ -25,8 +25,6 @@ from livekit.agents import (
)
from livekit.plugins import deepgram, silero
import voxtral_vllm_stt
load_dotenv()
logger = logging.getLogger("transcriber")
@@ -48,9 +46,6 @@ def create_stt_provider():
)
elif STT_PROVIDER == "kyutai":
_stt_instance = kyutai.STT(base_url=os.getenv("KYUTAI_STT_BASE_URL"))
elif STT_PROVIDER == "voxtral-vllm":
# The plugin resolves base_url / model / api_key from the environment.
_stt_instance = voxtral_vllm_stt.STT()
else:
raise ValueError(f"Unknown STT_PROVIDER: {STT_PROVIDER}")
+1 -1
View File
@@ -1,7 +1,7 @@
[project]
name = "agents"
version = "1.15.0"
version = "1.16.0"
requires-python = ">=3.12"
dependencies = [
"livekit-agents==1.4.5",
+15 -15
View File
@@ -9,7 +9,7 @@ resolution-markers = [
[[package]]
name = "agents"
version = "1.15.0"
version = "1.16.0"
source = { virtual = "." }
dependencies = [
{ name = "livekit-agents" },
@@ -28,7 +28,7 @@ dev = [
[package.metadata]
requires-dist = [
{ name = "livekit-agents", specifier = ">=1.4.5" },
{ name = "livekit-agents", specifier = "==1.4.5" },
{ name = "livekit-plugins-deepgram", specifier = "==1.4.5" },
{ name = "livekit-plugins-kyutai-lasuite", specifier = "==0.0.6" },
{ name = "livekit-plugins-silero", specifier = "==1.4.5" },
@@ -731,7 +731,7 @@ wheels = [
[[package]]
name = "livekit"
version = "1.1.7"
version = "1.1.2"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "aiofiles" },
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version = "1.4.5"
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[[package]]
-474
View File
@@ -1,474 +0,0 @@
"""LiveKit STT plugin for Voxtral Realtime served via vLLM (/v1/realtime).
vLLM exposes Voxtral Realtime over a WebSocket that follows the OpenAI Realtime
API protocol (not Mistral's proprietary realtime protocol).
"""
from __future__ import annotations
import asyncio
import base64
import json
import logging
import os
import weakref
from collections import deque
from dataclasses import dataclass, field
import websockets
from livekit.agents import (
DEFAULT_API_CONNECT_OPTIONS,
APIConnectionError,
APIConnectOptions,
APIStatusError,
stt,
utils,
)
from livekit.agents import (
vad as vad_module,
)
from livekit.agents.types import NOT_GIVEN, NotGivenOr
from livekit.agents.utils import is_given
logger = logging.getLogger("voxtral-vllm-stt")
SAMPLE_RATE = 16000
NUM_CHANNELS = 1
CHUNK_SAMPLES = 1600 # 100 ms @ 16 kHz mono
PREROLL_CHUNKS = 5 # keep 500 ms of audio before start of speech as detected by VAD
# Reconnect policy: exponential backoff capped at MAX, give up after MAX_ATTEMPTS
# consecutive failures (a successful handshake resets the counter).
RECONNECT_BACKOFF_BASE_S = 0.5
RECONNECT_BACKOFF_MAX_S = 8.0
RECONNECT_MAX_ATTEMPTS = 5
@dataclass
class _STTOptions:
base_url: str
model: str
api_key: str | None
target_streaming_delay_ms: int | None
@dataclass
class _PendingUtterance:
"""An utterance in flight on the shared websocket used for reconnect.
`sent_chunks` holds every chunk we have already enqueued for send on this
or a prior connection; on reconnect we replay them before resuming reads
from `queue`. vLLM concatenates `input_audio_buffer.append` events into a
single audio buffer per generation, so duplicates from a partial prior send
are harmless.
"""
queue: asyncio.Queue[bytes | None]
sent_chunks: list[bytes] = field(default_factory=list)
ended: bool = False
class STT(stt.STT):
"""LiveKit STT speaking the OpenAI Realtime protocol served by vLLM."""
def __init__(
self,
*,
base_url: NotGivenOr[str] = NOT_GIVEN,
model: NotGivenOr[str] = NOT_GIVEN,
api_key: NotGivenOr[str] = NOT_GIVEN,
target_streaming_delay_ms: NotGivenOr[int] = NOT_GIVEN,
vad: vad_module.VAD | None = None,
) -> None:
"""Build the STT.
Args:
base_url: WebSocket URL of the vLLM realtime endpoint, e.g.
ws://example:8000/v1/realtime. Falls back to $VOXTRAL_VLLM_BASE_URL.
model: Model name exposed by vLLM, default
mistralai/Voxtral-Mini-4B-Realtime-2602.
api_key: Optional bearer token. Falls back to $VOXTRAL_VLLM_API_KEY.
target_streaming_delay_ms: Target streaming delay in ms forwarded to
vLLM via session.update. Falls back to
$VOXTRAL_VLLM_TARGET_STREAMING_DELAY_MS, else server default.
vad: Voice Activity Detector. If omitted, Silero VAD is loaded.
"""
super().__init__(
capabilities=stt.STTCapabilities(streaming=True, interim_results=True)
)
resolved_url = (
base_url
if is_given(base_url)
else os.environ.get(
"VOXTRAL_VLLM_BASE_URL", "ws://127.0.0.1:8000/v1/realtime"
)
)
resolved_model = (
model
if is_given(model)
else os.environ.get(
"VOXTRAL_VLLM_MODEL", "mistralai/Voxtral-Mini-4B-Realtime-2602"
)
)
resolved_key = (
api_key if is_given(api_key) else os.environ.get("VOXTRAL_VLLM_API_KEY")
)
resolved_delay = (
target_streaming_delay_ms
if is_given(target_streaming_delay_ms)
else (
int(os.environ["VOXTRAL_VLLM_TARGET_STREAMING_DELAY_MS"])
if os.environ.get("VOXTRAL_VLLM_TARGET_STREAMING_DELAY_MS")
else None
)
)
if vad is None:
try:
from livekit.plugins.silero import VAD as SileroVAD # noqa: PLC0415
except ImportError as exc:
raise ImportError(
"livekit-plugins-silero is required for vLLM Voxtral realtime "
"(no server-side endpointing)."
) from exc
vad = SileroVAD.load()
self._vad = vad
self._opts = _STTOptions(
base_url=resolved_url,
model=resolved_model,
api_key=resolved_key,
target_streaming_delay_ms=resolved_delay,
)
self._streams: weakref.WeakSet[SpeechStream] = weakref.WeakSet()
@property
def model(self) -> str:
"""Return the configured vLLM model name."""
return self._opts.model
@property
def provider(self) -> str:
"""Return the provider identifier."""
return "vllm-voxtral-realtime"
async def _recognize_impl(self, *_args, **_kwargs) -> stt.SpeechEvent:
raise NotImplementedError(
"vLLM Voxtral Realtime STT only supports streaming recognition."
)
def stream(
self,
*,
conn_options: APIConnectOptions = DEFAULT_API_CONNECT_OPTIONS,
) -> SpeechStream:
"""Open a new streaming recognition stream."""
s = SpeechStream(
stt=self,
opts=self._opts,
vad_instance=self._vad,
conn_options=conn_options,
)
self._streams.add(s)
return s
class SpeechStream(stt.RecognizeStream):
def __init__(
self,
*,
stt: STT,
opts: _STTOptions,
vad_instance: vad_module.VAD,
conn_options: APIConnectOptions,
) -> None:
"""Init the speech stream."""
super().__init__(stt=stt, conn_options=conn_options, sample_rate=SAMPLE_RATE)
self._opts = opts
self._vad = vad_instance
self._utterance_q: asyncio.Queue[bytes | None] | None = None
self._speaking = False
self._preroll: deque[bytes] = deque(maxlen=PREROLL_CHUNKS)
# Voxtral realtime is strictly sequential: only one generation runs at a
# time, and a new `commit` is ignored while the previous one is still
# producing. We queue per-utterance audio buffers here and let the
# pipeline process them one by one on the shared websocket.
self._utterance_chan: asyncio.Queue[asyncio.Queue[bytes | None] | None] = (
asyncio.Queue()
)
@utils.log_exceptions(logger=logger)
async def _run(self) -> None:
vad_stream = self._vad.stream()
bstream = utils.audio.AudioByteStream(
sample_rate=SAMPLE_RATE,
num_channels=NUM_CHANNELS,
samples_per_channel=CHUNK_SAMPLES,
)
async def input_task() -> None:
async for data in self._input_ch:
if isinstance(data, self._FlushSentinel):
for frame in bstream.flush():
self._handle_chunk(frame.data.tobytes())
continue
vad_stream.push_frame(data)
for frame in bstream.write(data.data.tobytes()):
self._handle_chunk(frame.data.tobytes())
vad_stream.end_input()
async def vad_task() -> None:
async for ev in vad_stream:
if ev.type == vad_module.VADEventType.START_OF_SPEECH:
self._on_start_of_speech()
elif ev.type == vad_module.VADEventType.END_OF_SPEECH:
self._on_end_of_speech()
pipeline_t = asyncio.create_task(self._utterance_pipeline())
try:
await asyncio.gather(input_task(), vad_task())
# signal end-of-stream; pipeline finishes pending utterances first
self._utterance_chan.put_nowait(None)
await pipeline_t
except (APIStatusError, APIConnectionError, asyncio.CancelledError):
raise
except Exception as exc:
logger.exception("vLLM realtime stream failed")
raise APIConnectionError() from exc
finally:
if not pipeline_t.done():
pipeline_t.cancel()
try:
await pipeline_t
except asyncio.CancelledError:
# CancelledError is the expected flow on cancel()
pass
except Exception:
logger.exception("utterance pipeline failed during finalize")
await vad_stream.aclose()
def _handle_chunk(self, chunk: bytes) -> None:
self._preroll.append(chunk)
if self._speaking and self._utterance_q is not None:
self._utterance_q.put_nowait(chunk)
def _on_start_of_speech(self) -> None:
if self._speaking:
return
self._speaking = True
q: asyncio.Queue[bytes | None] = asyncio.Queue()
for chunk in self._preroll:
q.put_nowait(chunk)
self._utterance_q = q
self._utterance_chan.put_nowait(q)
self._event_ch.send_nowait(
stt.SpeechEvent(type=stt.SpeechEventType.START_OF_SPEECH)
)
def _on_end_of_speech(self) -> None:
if not self._speaking:
return
self._speaking = False
if self._utterance_q is not None:
self._utterance_q.put_nowait(None)
self._utterance_q = None
self._event_ch.send_nowait(
stt.SpeechEvent(type=stt.SpeechEventType.END_OF_SPEECH)
)
async def _handshake(self, ws: websockets.ClientConnection) -> str:
created = json.loads(await ws.recv())
if created.get("type") != "session.created":
raise APIStatusError(
f"expected session.created, got {created}",
status_code=500,
body=created,
)
session_update: dict = {"type": "session.update", "model": self._opts.model}
if self._opts.target_streaming_delay_ms is not None:
session_update["target_streaming_delay_ms"] = (
self._opts.target_streaming_delay_ms
)
await ws.send(json.dumps(session_update))
return created.get("id", "")
def _auth_headers(self) -> dict[str, str]:
if self._opts.api_key:
return {"Authorization": f"Bearer {self._opts.api_key}"}
return {}
async def _utterance_pipeline(self) -> None:
# Owns the websocket lifecycle. On drop, reopens and resumes the
# in-flight utterance (if any) by replaying its already-sent chunks.
pending: _PendingUtterance | None = None
attempt = 0
while True:
try:
async with websockets.connect(
self._opts.base_url,
additional_headers=self._auth_headers(),
open_timeout=self._conn_options.timeout,
) as ws:
request_id = await self._handshake(ws)
attempt = 0
while True:
if pending is None:
q = await self._utterance_chan.get()
if q is None:
return
pending = _PendingUtterance(queue=q)
await self._process_utterance(ws, pending, request_id)
pending = None
except (websockets.WebSocketException, OSError, TimeoutError) as exc:
attempt += 1
if attempt > RECONNECT_MAX_ATTEMPTS:
logger.exception(
"vLLM realtime: giving up after %d reconnect attempts",
RECONNECT_MAX_ATTEMPTS,
)
raise APIConnectionError() from exc
backoff = min(
RECONNECT_BACKOFF_BASE_S * (2 ** (attempt - 1)),
RECONNECT_BACKOFF_MAX_S,
)
if pending is None:
logger.warning(
"vLLM WS connection lost between utterances "
"(attempt %d/%d): %s; retrying in %.1fs",
attempt,
RECONNECT_MAX_ATTEMPTS,
exc,
backoff,
)
else:
logger.warning(
"vLLM WS dropped mid-utterance (%d chunks buffered, "
"ended=%s, attempt %d/%d): %s; retrying in %.1fs",
len(pending.sent_chunks),
pending.ended,
attempt,
RECONNECT_MAX_ATTEMPTS,
exc,
backoff,
)
await asyncio.sleep(backoff)
async def _process_utterance(
self,
ws: websockets.ClientConnection,
pending: _PendingUtterance,
request_id: str,
) -> None:
# Start a fresh generation. Safe to send here: the previous utterance's
# transcription.done has already been received (we await it below), so
# the server-side generation_task is done and won't ignore this commit.
await ws.send(json.dumps({"type": "input_audio_buffer.commit"}))
send_t = asyncio.create_task(self._send_audio(ws, pending))
try:
await self._receive_one_transcription(ws, request_id)
finally:
if not send_t.done():
send_t.cancel()
try:
await send_t
except (asyncio.CancelledError, websockets.WebSocketException):
pass
except Exception:
logger.exception("send-audio task failed during finalize")
@staticmethod
async def _send_audio(
ws: websockets.ClientConnection, pending: _PendingUtterance
) -> None:
# Replay anything already sent on a previous (now-dead) connection.
# sent_chunks is appended before send, so a chunk that failed to send
# last time is still present and gets retried here.
for chunk in pending.sent_chunks:
await ws.send(
json.dumps(
{
"type": "input_audio_buffer.append",
"audio": base64.b64encode(chunk).decode("ascii"),
}
)
)
if pending.ended:
await ws.send(
json.dumps({"type": "input_audio_buffer.commit", "final": True})
)
return
while True:
chunk = await pending.queue.get()
if chunk is None:
pending.ended = True
await ws.send(
json.dumps({"type": "input_audio_buffer.commit", "final": True})
)
return
pending.sent_chunks.append(chunk)
await ws.send(
json.dumps(
{
"type": "input_audio_buffer.append",
"audio": base64.b64encode(chunk).decode("ascii"),
}
)
)
async def _receive_one_transcription(
self, ws: websockets.ClientConnection, request_id: str
) -> None:
# Use recv() rather than `async for`: the latter swallows
# ConnectionClosed on close-mid-iteration, which would let a dropped
# WS look like a clean "no transcription" return.
current_text = ""
while True:
raw = await ws.recv()
data = json.loads(raw)
event_type = data.get("type")
if event_type == "transcription.delta":
delta = data.get("delta", "")
if not delta:
continue
current_text += delta
self._event_ch.send_nowait(
stt.SpeechEvent(
type=stt.SpeechEventType.INTERIM_TRANSCRIPT,
request_id=request_id,
alternatives=[stt.SpeechData(text=current_text, language="")],
)
)
elif event_type == "transcription.done":
final_text = data.get("text") or current_text
self._event_ch.send_nowait(
stt.SpeechEvent(
type=stt.SpeechEventType.FINAL_TRANSCRIPT,
request_id=request_id,
alternatives=[stt.SpeechData(text=final_text, language="")],
)
)
usage = data.get("usage") or {}
self._event_ch.send_nowait(
stt.SpeechEvent(
type=stt.SpeechEventType.RECOGNITION_USAGE,
request_id=request_id,
recognition_usage=stt.RecognitionUsage(
audio_duration=float(
usage.get("audio_seconds")
or usage.get("prompt_audio_seconds")
or 0
),
input_tokens=int(usage.get("prompt_tokens") or 0),
output_tokens=int(usage.get("completion_tokens") or 0),
),
)
)
return
elif event_type == "error":
err = data.get("error")
raise APIStatusError(str(err), status_code=500, body=data)
+1 -1
View File
@@ -7,7 +7,7 @@ build-backend = "uv_build"
[project]
name = "meet"
version = "1.15.0"
version = "1.16.0"
authors = [{ "name" = "DINUM", "email" = "dev@mail.numerique.gouv.fr" }]
classifiers = [
"Development Status :: 5 - Production/Stable",
+1 -1
View File
@@ -1173,7 +1173,7 @@ wheels = [
[[package]]
name = "meet"
version = "1.15.0"
version = "1.16.0"
source = { editable = "." }
dependencies = [
{ name = "aiohttp" },
+421 -462
View File
File diff suppressed because it is too large Load Diff
+3 -3
View File
@@ -1,7 +1,7 @@
{
"name": "meet",
"private": true,
"version": "1.15.0",
"version": "1.16.0",
"type": "module",
"scripts": {
"dev": "panda codegen && vite",
@@ -47,7 +47,7 @@
"wouter": "3.9.0"
},
"devDependencies": {
"@pandacss/dev": "1.8.2",
"@pandacss/dev": "1.11.1",
"@tanstack/eslint-plugin-query": "5.91.4",
"@tanstack/react-query-devtools": "5.91.3",
"@types/humanize-duration": "3.27.4",
@@ -62,7 +62,7 @@
"eslint-plugin-jsx-a11y": "6.10.2",
"eslint-plugin-react-hooks": "5.2.0",
"eslint-plugin-react-refresh": "0.4.20",
"postcss": "8.5.10",
"postcss": "8.5.14",
"prettier": "3.8.1",
"typescript": "5.8.3",
"vite": "7.3.2",
@@ -12,7 +12,7 @@ const controlBarRegion = cva({
variants: {
mobile: {
true: {
justifyContent: 'space-between',
justifyContent: 'center',
width: '330px',
},
},
@@ -75,15 +75,15 @@ const useTranscriptionState = () => {
const segment = segments[0]
setTranscriptionSegments((prevSegments) => {
const existingIndex = prevSegments.findIndex(
(s: TranscriptionSegmentWithParticipant) => s.id === segment.id
)
if (existingIndex === -1) {
return [...prevSegments, { participant, ...segment }]
}
const next = prevSegments.slice()
next[existingIndex] = { ...next[existingIndex], ...segment }
return next
const existingSegmentIds = new Set(prevSegments.map((s) => s.id))
if (existingSegmentIds.has(segment.id)) return prevSegments
return [
...prevSegments,
{
participant: participant,
...segment,
},
]
})
}
+5 -5
View File
@@ -1,12 +1,12 @@
{
"name": "mail_mjml",
"version": "1.15.0",
"version": "1.16.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "mail_mjml",
"version": "1.15.0",
"version": "1.16.0",
"license": "MIT",
"dependencies": {
"@html-to/text-cli": "0.5.4",
@@ -1588,9 +1588,9 @@
}
},
"node_modules/semver": {
"version": "7.7.4",
"resolved": "https://registry.npmjs.org/semver/-/semver-7.7.4.tgz",
"integrity": "sha512-vFKC2IEtQnVhpT78h1Yp8wzwrf8CM+MzKMHGJZfBtzhZNycRFnXsHk6E5TxIkkMsgNS7mdX3AGB7x2QM2di4lA==",
"version": "7.8.0",
"resolved": "https://registry.npmjs.org/semver/-/semver-7.8.0.tgz",
"integrity": "sha512-AcM7dV/5ul4EekoQ29Agm5vri8JNqRyj39o0qpX6vDF2GZrtutZl5RwgD1XnZjiTAfncsJhMI48QQH3sN87YNA==",
"license": "ISC",
"bin": {
"semver": "bin/semver.js"
+1 -1
View File
@@ -1,6 +1,6 @@
{
"name": "mail_mjml",
"version": "1.15.0",
"version": "1.16.0",
"description": "An util to generate html and text django's templates from mjml templates",
"type": "module",
"dependencies": {
+2 -2
View File
@@ -1,12 +1,12 @@
{
"name": "sdk",
"version": "1.15.0",
"version": "1.16.0",
"lockfileVersion": 3,
"requires": true,
"packages": {
"": {
"name": "sdk",
"version": "1.15.0",
"version": "1.16.0",
"license": "ISC",
"workspaces": [
"./library",
+1 -1
View File
@@ -1,6 +1,6 @@
{
"name": "sdk",
"version": "1.15.0",
"version": "1.16.0",
"author": "",
"license": "ISC",
"description": "",
+1 -1
View File
@@ -1,7 +1,7 @@
[project]
name = "summary"
version = "1.15.0"
version = "1.16.0"
dependencies = [
"fastapi[standard]>=0.105.0",
"uvicorn>=0.24.0",
+3
View File
@@ -105,6 +105,9 @@ class Settings(BaseSettings):
# Speaker to user assignment
is_resolve_speaker_identities_enabled: bool = True
resolve_speaker_identities_default_overlap_threshold: float = 0.5
resolve_speaker_identities_enable_split_on_words: bool = True
resolve_speaker_identities_max_word_duration: float = 1 # seconds
# Webhook-related settings
webhook_max_retries: int = 2
+118 -15
View File
@@ -14,11 +14,11 @@ from dataclasses import dataclass, field
from datetime import datetime
from typing import Any
logger = logging.getLogger(__name__)
from summary.core.config import get_settings
# Minimum fraction of a speaker's total duration that must overlap with a
# participant's VAD to accept the assignment.
DEFAULT_OVERLAP_THRESHOLD = 0.5
settings = get_settings()
logger = logging.getLogger(__name__)
@dataclass
@@ -74,7 +74,7 @@ class AssignmentResult:
f" ({item['speaker']})" if name_to_speaker_count[name] > 1 else ""
) # Add suffix only if there are multiple detected speakers per user
return {**item, "speaker": f"{name}{suffix}"}
return item
return {**item}
def _process_segment(
item: dict[str, Any], include_words: bool = False
@@ -138,6 +138,78 @@ def _overlap_duration(
return overlap
def _format_timelines_debug(
participant_timelines: dict[str, list[Interval]],
participant_names: dict[str, str],
speaker_timelines: dict[str, list[Interval]],
) -> str:
"""Render participant and speaker timelines side-by-side for debugging.
Each row is the slice between two consecutive interval boundaries
(drawn from both sides). A filled cell marks an active participant
(left block) or speaker (right block) during that slice, so vertical
alignment makes overlap visually obvious.
"""
participant_ids = sorted(participant_timelines.keys())
speaker_labels = sorted(speaker_timelines.keys())
if not participant_ids and not speaker_labels:
return "(no timelines)"
boundaries: set[float] = set()
for intervals in (*participant_timelines.values(), *speaker_timelines.values()):
for iv in intervals:
boundaries.add(iv.start)
boundaries.add(iv.end)
sorted_boundaries = sorted(boundaries)
if len(sorted_boundaries) < 2:
return "(no intervals)"
p_headers = [participant_names.get(pid, pid) for pid in participant_ids]
s_headers = list(speaker_labels)
p_widths = [max(len(h), 3) for h in p_headers]
s_widths = [max(len(h), 3) for h in s_headers]
def _active(intervals: list[Interval], lo: float, hi: float) -> bool:
mid = (lo + hi) / 2
return any(iv.start <= mid < iv.end for iv in intervals)
def _cells(
intervals_list: list[list[Interval]],
widths: list[int],
lo: float,
hi: float,
) -> str:
return " ".join(
("" * w if _active(iv, lo, hi) else "·" * w)
for iv, w in zip(intervals_list, widths, strict=True)
)
p_iv_list = [participant_timelines[pid] for pid in participant_ids]
s_iv_list = [speaker_timelines[sl] for sl in speaker_labels]
time_col = "[ start → end]"
p_hdr = (
" ".join(h.center(w) for h, w in zip(p_headers, p_widths, strict=True))
or "(none)"
)
s_hdr = (
" ".join(h.center(w) for h, w in zip(s_headers, s_widths, strict=True))
or "(none)"
)
sep = " || "
lines = [
f"{time_col} {p_hdr}{sep}{s_hdr}",
"-" * (len(time_col) + 2 + len(p_hdr) + len(sep) + len(s_hdr)),
]
for lo, hi in zip(sorted_boundaries, sorted_boundaries[1:], strict=False):
time_str = f"[{lo:8.2f}{hi:8.2f}]"
p_row = _cells(p_iv_list, p_widths, lo, hi) or " " * len(p_hdr)
s_row = _cells(s_iv_list, s_widths, lo, hi) or " " * len(s_hdr)
lines.append(f"{time_str} {p_row}{sep}{s_row}")
return "\n".join(lines)
def _build_participant_timelines(
metadata: dict[str, Any],
recording_start_datetime: datetime,
@@ -199,24 +271,50 @@ def _build_participant_timelines(
return intervals, participants_info
def _build_speaker_timelines(
transcription: Any,
) -> dict[str, list[Interval]]:
def _build_speaker_timelines(transcription: Any) -> dict[str, list[Interval]]:
"""Build interval timelines from WhisperX transcription segments."""
intervals: dict[str, list[Interval]] = {}
segments = transcription.segments if hasattr(transcription, "segments") else []
max_word_duration = settings.resolve_speaker_identities_max_word_duration
for segment in segments:
speaker = segment.get("speaker")
if speaker is None:
continue
intervals.setdefault(speaker, []).append(
Interval(segment["start"], segment["end"])
)
words = [
w
for w in segment.get("words", [])
if w.get("start") is not None and w.get("end") is not None
]
if not words:
intervals.setdefault(speaker, []).append(
Interval(segment["start"], segment["end"])
)
continue
start_time: float | None = segment["start"]
for word in words:
if start_time is None:
start_time = word["start"]
if not settings.resolve_speaker_identities_enable_split_on_words:
continue
if word["end"] - word["start"] > max_word_duration:
end_time = word["start"] + max_word_duration
if end_time > start_time:
intervals.setdefault(speaker, []).append(
Interval(start_time, end_time)
)
start_time = None
if start_time is not None:
last = words[-1]
end_time = min(last["end"], last["start"] + max_word_duration)
if end_time > start_time:
intervals.setdefault(speaker, []).append(Interval(start_time, end_time))
for speaker, speaker_intervals in intervals.items():
intervals[speaker] = _merge_intervals(speaker_intervals)
return intervals
@@ -225,7 +323,7 @@ def resolve_speaker_identities(
transcription: Any,
recording_start_datetime: datetime,
recording_end_datetime: datetime,
overlap_threshold: float = DEFAULT_OVERLAP_THRESHOLD,
overlap_threshold: float = settings.resolve_speaker_identities_default_overlap_threshold, # noqa: E501
) -> AssignmentResult:
"""Match WhisperX speaker labels to participants.
@@ -247,9 +345,14 @@ def resolve_speaker_identities(
speaker_timelines = _build_speaker_timelines(transcription)
logger.debug(
"Assignment inputs: %d participants, %d speakers",
"Assignment inputs: %d participants, %d speakers\n%s\n%s\n%s",
len(participant_timelines),
len(speaker_timelines),
participant_timelines,
speaker_timelines,
_format_timelines_debug(
participant_timelines, participant_names, speaker_timelines
),
)
result = AssignmentResult()
+180
View File
@@ -4,10 +4,12 @@ import math
from dataclasses import dataclass, field
from datetime import datetime
from summary.core import user_assign
from summary.core.user_assign import (
AssignmentResult,
Interval,
SpeakerAssignment,
_build_speaker_timelines,
_merge_intervals,
_overlap_duration,
_total_duration,
@@ -73,12 +75,22 @@ DIARIZATION_SINGLE_SPEAKER = FakeTranscription(
"end": 3.545,
"text": " The stale smell.",
"speaker": "SPEAKER_00",
"words": [
{"word": "The", "start": 1.363, "end": 1.8},
{"word": "stale", "start": 1.8, "end": 2.7},
{"word": "smell.", "start": 2.7, "end": 3.545},
],
},
{
"start": 4.466,
"end": 6.247,
"text": "It takes heat.",
"speaker": "SPEAKER_00",
"words": [
{"word": "It", "start": 4.466, "end": 4.7},
{"word": "takes", "start": 4.7, "end": 5.5},
{"word": "heat.", "start": 5.5, "end": 6.247},
],
},
],
)
@@ -165,6 +177,174 @@ class TestTotalDuration:
assert math.isclose(_total_duration([]), 0.0)
class TestBuildSpeakerTimelines:
"""Tests for _build_speaker_timelines."""
def test_segment_without_words_falls_back_to_segment_bounds(self):
"""Segments missing a `words` key use the segment start/end as one interval."""
transcription = FakeTranscription(
segments=[{"start": 1.5, "end": 3.5, "speaker": "SPEAKER_00"}],
)
result = _build_speaker_timelines(transcription)
assert result == {"SPEAKER_00": [Interval(1.5, 3.5)]}
def test_segment_with_only_none_word_timestamps_falls_back(self):
"""If every word has None start/end, fall back to segment bounds."""
transcription = FakeTranscription(
segments=[
{
"start": 1.0,
"end": 4.0,
"speaker": "SPEAKER_00",
"words": [
{"word": "hi", "start": None, "end": None},
{"word": "there", "start": None, "end": None},
],
},
],
)
result = _build_speaker_timelines(transcription)
assert result == {"SPEAKER_00": [Interval(1.0, 4.0)]}
def test_short_words_only_uses_segment_start_and_last_word_end(self):
"""With no overly long words, the interval runs segment start to end."""
transcription = FakeTranscription(
segments=[
{
"start": 1.0,
"end": 5.0,
"speaker": "SPEAKER_00",
"words": [
{"word": "a", "start": 1.0, "end": 1.3},
{"word": "b", "start": 1.4, "end": 1.7},
{"word": "c", "start": 1.8, "end": 2.1},
],
},
],
)
result = _build_speaker_timelines(transcription)
# Tail: min(2.1, 1.8 + 1.0) = 2.1
assert result == {"SPEAKER_00": [Interval(1.0, 2.1)]}
def test_long_word_caps_interval_at_max_duration(self):
"""A word longer than the max-word-duration cap truncates the segment."""
max_word_duration = (
user_assign.settings.resolve_speaker_identities_max_word_duration
)
transcription = FakeTranscription(
segments=[
{
"start": 0.0,
"end": max_word_duration + 7,
"speaker": "SPEAKER_00",
"words": [
{
"word": "pause",
"start": 0.0,
"end": max_word_duration + 7,
},
],
},
],
)
result = _build_speaker_timelines(transcription)
assert result == {"SPEAKER_00": [Interval(0.0, max_word_duration)]}
def test_long_word_in_middle_splits_segment(self):
"""Short words around a long word produce two intervals (before-cap + after)."""
transcription = FakeTranscription(
segments=[
{
"start": 0.0,
"end": 20.0,
"speaker": "SPEAKER_00",
"words": [
{"word": "a", "start": 0.0, "end": 0.5},
{"word": "long", "start": 1.0, "end": 15.0},
{"word": "z", "start": 18.0, "end": 18.4},
],
},
],
)
result = _build_speaker_timelines(transcription)
# First emit: (0.0, 1.0 + 1.0). Then start_time resets, picks up at "z" (18.0).
# Tail: min(18.4, 18.0 + 1.0) = 18.4. So second interval is (18.0, 18.4).
assert result == {
"SPEAKER_00": [Interval(0.0, 2.0), Interval(18.0, 18.4)],
}
def test_tail_word_is_capped_at_max_duration(self):
"""The trailing word's end is capped at word.start + max_word_duration."""
transcription = FakeTranscription(
segments=[
{
"start": 0.0,
"end": 50.0,
"speaker": "SPEAKER_00",
"words": [
{"word": "a", "start": 0.0, "end": 0.4},
# Last word ends inside the cap, so the cap doesn't apply.
{"word": "b", "start": 1.0, "end": 1.5},
],
},
],
)
result = _build_speaker_timelines(transcription)
# Tail: min(1.5, 1.0 + 1.0) = 1.5
assert result == {"SPEAKER_00": [Interval(0.0, 1.5)]}
def test_split_on_words_disabled_keeps_segment_as_one_interval(self, monkeypatch):
"""With splitting disabled, long words don't split the interval."""
monkeypatch.setattr(
user_assign,
"settings",
user_assign.settings.model_copy(
update={"resolve_speaker_identities_enable_split_on_words": False},
),
)
transcription = FakeTranscription(
segments=[
{
"start": 0.0,
"end": 20.0,
"speaker": "SPEAKER_00",
"words": [
{"word": "a", "start": 0.0, "end": 0.5},
{"word": "long", "start": 1.0, "end": 15.0},
{"word": "z", "start": 18.0, "end": 18.4},
],
},
],
)
result = _build_speaker_timelines(transcription)
# No mid-segment split; tail caps at min(18.4, 18.0 + 1.0) = 18.4.
assert result == {"SPEAKER_00": [Interval(0.0, 18.4)]}
def test_multiple_speakers_keep_separate_timelines(self):
"""Segments from different speakers populate independent timeline entries."""
transcription = FakeTranscription(
segments=[
{
"start": 0.0,
"end": 1.0,
"speaker": "SPEAKER_00",
"words": [{"word": "hi", "start": 0.0, "end": 0.5}],
},
{
"start": 2.0,
"end": 3.0,
"speaker": "SPEAKER_01",
"words": [{"word": "yo", "start": 2.0, "end": 2.5}],
},
],
)
result = _build_speaker_timelines(transcription)
assert result == {
"SPEAKER_00": [Interval(0.0, 0.5)],
"SPEAKER_01": [Interval(2.0, 2.5)],
}
class TestResolveSpeakerIdentities:
"""Tests for resolve_speaker_identities."""