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feat(api): use Tencent 16k_zh_large ASR and remove local Whisper

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Standardize ASR on Tencent's dialect-capable engine across all environments,
drop faster-whisper from dependencies and deployment images, and add an
expo-sqlite iOS vendor sync plus pod install in prebuild to prevent native
build failures after npm install.

Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
Kevin
2026-05-25 10:21:41 +08:00
parent 4f0a314656
commit 22d282dc01
23 changed files with 91 additions and 561 deletions
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1
.gitignore vendored
View File

@@ -64,7 +64,6 @@ dist/
*.egg-info/
app-expo/coverage/
# 本地 ASR 模型缓存（Whisper 每次启动从该目录加载）
api/models/certs/
api/models/whisper/

4
README.md
View File

@@ -28,7 +28,7 @@ life-echo/
│ │ ├── internal_main.py # 内部评测 API 入口
│ │ ├── core/ # 配置、DB、日志、Redis、LLM 调用等
│ │ ├── ports/ # 供应商能力协议（ASR/TTS/LLM/…）
│ │ ├── adapters/ # ports 的具体实现（腾讯、OpenAI、Whisper 等）
│ │ ├── adapters/ # ports 的具体实现（腾讯、OpenAI 等）
│ │ ├── agents/ # 对话与回忆录编排 Agent
│ │ ├── features/ # 按域拆分：auth、conversation、memoir、memory、evaluation…
│ │ └── tasks/ # Celery 任务入口
@@ -103,7 +103,7 @@ uv run celery -A tasks.celery_app worker --loglevel=info --pool=solo
| SQLAlchemy + Alembic | 2.0.36 / 1.18+ | ORM 与数据库 schema migration |
| Redis | 7 | 缓存和会话存储 |
| Celery | 5.3 | 异步任务队列 |
| OpenAI API | - | ASR（语音识别）、TTS（语音合成） |
| 腾讯云 ASR/TTS | - | 语音识别（`16k_zh_large`）、语音合成 |
| DeepSeek API | - | LLM（大语言模型） |
| ReportLab + WeasyPrint | - | PDF 生成 |

4
api/Dockerfile
View File

@@ -27,10 +27,6 @@ RUN uv sync --frozen --no-dev --no-install-project
# 复制应用代码
COPY . .
# 构建时预下载 faster-whisper 模型
ARG ASR_MODEL_SIZE=small
RUN uv run python -c "from faster_whisper import WhisperModel; WhisperModel('${ASR_MODEL_SIZE}', device='cpu', compute_type='int8', download_root='/app/models/whisper')"
# 非 root 用户
RUN useradd -m -u 1000 appuser && chown -R appuser:appuser /app
USER appuser

4
api/README.md
View File

@@ -29,7 +29,7 @@ Life Echo API 是一个智能对话系统，通过 WebSocket 实时连接，使
- **数据库**: PostgreSQL 17 + SQLAlchemy 2.0.36 (asyncpg)
- **缓存/队列**: Redis 7 + Celery 5.3
- **PDF 生成**: ReportLab 4.2.2 + WeasyPrint 62.3
- **ASR/TTS**: OpenAI Whisper API
- **ASR/TTS**: 腾讯云 ASR（`16k_zh_large`）/ TTS
- **认证**: JWT (python-jose) + bcrypt
- **其他**: Pydantic, python-dotenv
- **可观测性**: OpenTelemetry → Grafana LGTM（Tempo / Prometheus / Loki），见 [`docs/observability.md`](docs/observability.md)
@@ -533,7 +533,7 @@ ws://localhost:8000/ws/conversation/{conversation_id}?token={access_token}
### 3. 语音服务
- **ASR (语音识别)**: 使用 OpenAI Whisper API 将音频转为文本
- **ASR (语音识别)**: 腾讯云一句话识别（引擎 `16k_zh_large`，含多方言）
- **TTS (语音合成)**: 使用 OpenAI TTS API 将文本转为语音
### 4. PDF 生成

11
api/app/adapters/asr/tencent_asr.py
View File

@@ -11,9 +11,16 @@ logger = get_logger(__name__)
class TencentASRProvider:
def __init__(self, secret_id: str, secret_key: str):
def __init__(
self,
secret_id: str,
secret_key: str,
*,
engine_type: str = "16k_zh_large",
):
self._secret_id = secret_id
self._secret_key = secret_key
self._engine_type = engine_type
self._client = None
def _get_client(self):
@@ -54,7 +61,7 @@ class TencentASRProvider:
audio_base64 = base64.b64encode(audio).decode("utf-8")
req = models.SentenceRecognitionRequest()
req.EngSerViceType = "16k_zh"
req.EngSerViceType = self._engine_type
req.SourceType = 1
# 小写；与文档一致。iOS 常见为 m4a(AAC) 容器，与 16k 引擎匹配
req.VoiceFormat = (format or "m4a").lower()

203
api/app/adapters/asr/whisper_local.py
View File

@@ -1,203 +0,0 @@
"""Local faster-whisper ASR adapter — implements ASRProvider port."""
from __future__ import annotations
import asyncio
import os
import re
import tempfile
from typing import Any, Iterable
from app.core.business_telemetry import business_span
from app.core.logging import get_logger
from app.ports.asr import ASRTranscriptionError
logger = get_logger(__name__)
_SUBTITLE_WATERMARK_RE = re.compile(
r"(字幕|听译|压制|字幕组).{0,20}(by|BY|By)|字幕\s*by",
re.UNICODE,
)
def _looks_like_subtitle_hallucination(text: str) -> bool:
"""静音时第二遍易吐出视频字幕水印；仅丢弃此类短句。"""
t = (text or "").strip()
if len(t) > 48:
return False
if _SUBTITLE_WATERMARK_RE.search(t):
return True
if len(t) <= 12 and "字幕" in t and not re.search(r"[？?！!。，、]", t):
return True
return False
def _join_segment_text(segments: Iterable[Any]) -> tuple[str, int]:
segs = list(segments)
return "".join(str(getattr(seg, "text", "") or "") for seg in segs).strip(), len(
segs
)
_DEFAULT_CACHE_DIR = os.path.normpath(
os.path.join(
os.path.dirname(os.path.abspath(__file__)),
"..",
"..",
"..",
"models",
"whisper",
)
)
class WhisperASRProvider:
def __init__(
self,
model_size: str = "small",
device: str = "auto",
compute_type: str = "auto",
cache_dir: str = "",
):
self._model_size = model_size
self._device = device
self._compute_type = compute_type
self._cache_dir = cache_dir
self._model = None
def _load_model(self) -> bool:
if self._model is not None:
return True
try:
from faster_whisper import WhisperModel
device = self._device
compute_type = self._compute_type
if device == "auto":
try:
import torch # type: ignore[import-untyped]
device = "cuda" if torch.cuda.is_available() else "cpu"
except ImportError:
device = "cpu"
if compute_type == "auto":
compute_type = "float16" if device == "cuda" else "int8"
download_root = self._cache_dir or _DEFAULT_CACHE_DIR
local_files_only = bool(self._cache_dir)
os.makedirs(download_root, exist_ok=True)
self._model = WhisperModel(
self._model_size,
device=device,
compute_type=compute_type,
download_root=download_root,
local_files_only=local_files_only,
)
return True
except Exception as e:
logger.error("Failed to load Whisper model: {}", e)
return False
def ensure_ready(self) -> bool:
return self._load_model()
async def transcribe(self, audio: bytes, format: str = "m4a") -> str:
with business_span("asr.transcribe", provider="whisper"):
return await self._transcribe_inner(audio, format)
async def _transcribe_inner(self, audio: bytes, format: str) -> str:
# 与 v1.1.0 相同的单次 transcribe；推理放线程池，避免阻塞 asyncio（tag 上为同步调用）。
self._load_model()
if not self._model:
raise ASRTranscriptionError("Whisper model not loaded")
model = self._model
def _sync_transcribe() -> str:
tmp_path = None
try:
with tempfile.NamedTemporaryFile(
suffix=f".{format}", delete=False
) as tmp:
tmp.write(audio)
tmp_path = tmp.name
segments, _info = model.transcribe(
tmp_path,
language="zh",
beam_size=5,
vad_filter=True,
vad_parameters={
"min_silence_duration_ms": 500,
"threshold": 0.35,
"min_speech_duration_ms": 200,
},
)
text, pass1_seg_count = _join_segment_text(segments)
used_second_pass = False
pass2_seg_count = 0
pass3_seg_count = 0
if not text:
logger.info(
"Whisper VAD pass 无文本，关闭 VAD 再试一次（短录音易被 VAD 判为静音）"
)
segments2, _info2 = model.transcribe(
tmp_path,
language="zh",
beam_size=5,
vad_filter=False,
condition_on_previous_text=False,
# 略抬高：减少边界片段被标成 no_speech 而整段为空
no_speech_threshold=0.85,
)
raw2, pass2_seg_count = _join_segment_text(segments2)
used_second_pass = True
if raw2 and _looks_like_subtitle_hallucination(raw2):
logger.info(
"Whisper 丢弃疑似字幕水印幻听: {!r}",
raw2[:120],
)
text = ""
else:
text = raw2
if not text and used_second_pass:
try:
from faster_whisper import decode_audio
audio_np = decode_audio(tmp_path, sampling_rate=16000)
segments3, _info3 = model.transcribe(
audio_np,
language="zh",
beam_size=5,
vad_filter=False,
condition_on_previous_text=False,
no_speech_threshold=0.85,
)
raw3, pass3_seg_count = _join_segment_text(segments3)
if raw3 and _looks_like_subtitle_hallucination(raw3):
logger.info(
"Whisper decode_audio 回退仍是疑似字幕水印幻听: {!r}",
raw3[:120],
)
elif raw3:
text = raw3
except Exception as ex:
logger.warning("Whisper decode_audio 回退失败: {}", ex)
return text
except ASRTranscriptionError:
raise
except Exception as e:
logger.error("Whisper transcribe failed: {}", e)
raise ASRTranscriptionError(f"Whisper transcribe failed: {e!s}") from e
finally:
if tmp_path and os.path.exists(tmp_path):
try:
os.remove(tmp_path)
except OSError:
pass
return await asyncio.to_thread(_sync_transcribe)

9
api/app/core/app_config_models.py
View File

@@ -1,5 +1,7 @@
"""Pydantic models for TOML-backed application configuration (non-secret SSOT)."""
from typing import Literal
from pydantic import BaseModel, ConfigDict, Field
@@ -201,11 +203,8 @@ class LlmConfig(BaseModel):
class AsrConfig(BaseModel):
model_config = ConfigDict(extra="forbid")
provider: str = "whisper"
model_size: str = "small"
device: str = "auto"
compute_type: str = "auto"
model_cache_dir: str = ""
provider: Literal["tencent"] = "tencent"
engine_type: Literal["16k_zh_large"] = "16k_zh_large"
class TtsConfig(BaseModel):

11
api/app/core/dependencies.py
View File

@@ -102,21 +102,12 @@ def get_tts_provider() -> TTSProvider:
@lru_cache
def get_asr_provider() -> ASRProvider:
if asr_defaults.provider == "tencent":
from app.adapters.asr.tencent_asr import TencentASRProvider
return TencentASRProvider(
secret_id=settings.tencent_secret_id,
secret_key=settings.tencent_secret_key,
)
from app.adapters.asr.whisper_local import WhisperASRProvider
return WhisperASRProvider(
model_size=asr_defaults.model_size,
device=asr_defaults.device,
compute_type=asr_defaults.compute_type,
cache_dir=asr_defaults.model_cache_dir,
engine_type=asr_defaults.engine_type,
)

8
api/app/main.py
View File

@@ -85,12 +85,10 @@ async def lifespan(app: FastAPI):
else:
asr_ready = True
if asr_ready:
name = (
"腾讯云一句话识别"
if asr_defaults.provider == "tencent"
else "本地 Whisper"
logger.info(
"ASR 服务已就绪（腾讯云一句话识别，引擎 {}）",
asr_defaults.engine_type,
)
logger.info("ASR 服务已就绪（{}）", name)
else:
logger.warning("ASR 服务未就绪，语音转写将不可用")
except Exception as e:

7
api/config/default.toml
View File

@@ -176,11 +176,8 @@ embedding_base_url = "https://open.bigmodel.cn/api/paas/v4"
embedding_model = "embedding-3"
[asr]
provider = "whisper"
model_size = "small"
device = "auto"
compute_type = "auto"
model_cache_dir = ""
provider = "tencent"
engine_type = "16k_zh_large"
[tts]
provider = "tencent"

1
api/docker-compose.yml
View File

@@ -71,7 +71,6 @@ services:
env_file:
- .env
environment:
- ASR_MODEL_CACHE_DIR=/app/models/whisper
- APP_ENV=${APP_ENV:-production}
- REDIS_URL=redis://redis:6379/0
- CELERY_REDIS_URL=redis://redis:6379/1

1
api/docs/ai-touchpoints.md
View File

@@ -5,7 +5,6 @@ Regenerate: `uv run python api/scripts/ai_touchpoints_scan.py --markdown api/doc
| File | Tags |
|------|------|
| `api/app/adapters/asr/tencent_asr.py` | `ports_ai` |
| `api/app/adapters/asr/whisper_local.py` | `ports_ai` |
| `api/app/adapters/embedding/zhipu.py` | `embedding` |
| `api/app/adapters/llm/deepseek.py` | `json_llm_helpers`, `langchain`, `ports_ai` |
| `api/app/adapters/llm/deepseek_eval_judge.py` | `langchain` |

1
api/pyproject.toml
View File

@@ -10,7 +10,6 @@ dependencies = [
"celery[redis]>=5.6.2",
"cos-python-sdk-v5>=1.9.41",
"fastapi[standard]>=0.135.1",
"faster-whisper>=1.2.1",
"flower>=2.0.1",
"greenlet>=3.3.2",
"httpx>=0.28.1",

3
api/requirements.txt
View File

@@ -34,9 +34,6 @@ httpx==0.27.0
python-jose[cryptography]==3.3.0
bcrypt>=4.0.0
# Audio Processing - Local Whisper ASR
faster-whisper>=1.0.0
# Image Processing
Pillow>=10.0.0

5
api/tests/test_default_toml_legacy_parity.py
View File

@@ -16,8 +16,7 @@ def test_default_toml_matches_legacy_settings_defaults() -> None:
assert cfg.story.image_min_body_chars == 400
assert cfg.asr.provider == "whisper"
assert cfg.asr.device == "auto"
assert cfg.asr.compute_type == "auto"
assert cfg.asr.provider == "tencent"
assert cfg.asr.engine_type == "16k_zh_large"
assert cfg.misc.tencent_sms_template_param_count == 2

3
api/tests/test_infra_regressions.py
View File

@@ -86,7 +86,7 @@ async def test_tencent_asr_transcribe_uses_to_thread(
monkeypatch.setitem(sys.modules, "tencentcloud.asr.v20190614", package_module)
monkeypatch.setattr(asyncio, "to_thread", fake_to_thread)
provider = TencentASRProvider("sid", "skey")
provider = TencentASRProvider("sid", "skey", engine_type="16k_zh_large")
client = FakeClient()
monkeypatch.setattr(provider, "_get_client", lambda: client)
@@ -98,5 +98,6 @@ async def test_tencent_asr_transcribe_uses_to_thread(
assert getattr(fn, "__self__", None) is client
assert getattr(fn, "__name__", "") == "SentenceRecognition"
request = args[0]
assert request.EngSerViceType == "16k_zh_large"
assert request.VoiceFormat == "m4a"
assert request.DataLen == len(b"fake-audio")

61
api/tests/test_whisper_local.py
View File

@@ -1,61 +0,0 @@
import asyncio
import sys
from types import SimpleNamespace
import pytest
from app.adapters.asr.whisper_local import (
WhisperASRProvider,
_looks_like_subtitle_hallucination,
)
def test_subtitle_watermark_detection() -> None:
assert _looks_like_subtitle_hallucination("字幕by索兰娅") is True
assert _looks_like_subtitle_hallucination("今天想聊聊童年往事") is False
@pytest.mark.asyncio
async def test_transcribe_retries_decode_audio_after_discarded_pass2(
monkeypatch: pytest.MonkeyPatch,
) -> None:
class DummyModel:
def __init__(self) -> None:
self.calls: list[object] = []
def transcribe(self, audio: object, **_: object):
self.calls.append(audio)
n = len(self.calls)
if n == 1:
return iter([]), SimpleNamespace()
if n == 2:
return iter([SimpleNamespace(text="字幕by索兰娅")]), SimpleNamespace()
if n == 3:
assert audio == "decoded-audio"
return (
iter([SimpleNamespace(text="你好，今天想聊聊童年。")]),
SimpleNamespace(),
)
raise AssertionError(f"unexpected transcribe call #{n}")
async def fake_to_thread(fn):
return fn()
def fake_decode_audio(_: str, sampling_rate: int = 16000):
assert sampling_rate == 16000
return "decoded-audio"
monkeypatch.setattr(asyncio, "to_thread", fake_to_thread)
monkeypatch.setitem(
sys.modules,
"faster_whisper",
SimpleNamespace(decode_audio=fake_decode_audio),
)
provider = WhisperASRProvider()
provider._model = DummyModel()
text = await provider.transcribe(b"fake-audio", format="m4a")
assert text == "你好，今天想聊聊童年。"
assert len(provider._model.calls) == 3

243
api/uv.lock generated
View File

@@ -81,7 +81,6 @@ dependencies = [
{ name = "celery", extra = ["redis"] },
{ name = "cos-python-sdk-v5" },
{ name = "fastapi", extra = ["standard"] },
{ name = "faster-whisper" },
{ name = "flower" },
{ name = "greenlet" },
{ name = "httpx" },
@@ -132,7 +131,6 @@ requires-dist = [
{ name = "celery", extras = ["redis"], specifier = ">=5.6.2" },
{ name = "cos-python-sdk-v5", specifier = ">=1.9.41" },
{ name = "fastapi", extras = ["standard"], specifier = ">=0.135.1" },
{ name = "faster-whisper", specifier = ">=1.2.1" },
{ name = "flower", specifier = ">=2.0.1" },
{ name = "greenlet", specifier = ">=3.3.2" },
{ name = "httpx", specifier = ">=0.28.1" },
@@ -185,42 +183,6 @@ wheels = [
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]
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name = "av"
version = "16.1.0"
source = { registry = "https://pypi.org/simple" }
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wheels = [
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name = "tenacity"
version = "9.1.4"
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name = "tornado"
version = "6.5.5"

3
app-expo/package.json
View File

@@ -4,13 +4,14 @@
"version": "1.2.0",
"scripts": {
"use-env": "node scripts/use-env.js",
"postinstall": "node scripts/ensure-expo-sqlite-ios.js",
"prestart": "npm run use-env -- development",
"start": "expo start",
"start:staging": "npm run use-env -- staging && expo start",
"start:prod": "npm run use-env -- production && expo start",
"reset-project": "node ./scripts/reset-project.js",
"android": "npm run use-env -- development && expo run:android",
"ios": "npm run use-env -- development && expo run:ios",
"ios": "npm run use-env -- development && node scripts/ensure-expo-sqlite-ios.js && expo run:ios",
"ios:prebuild": "bash scripts/ios-prebuild.sh staging",
"ios:prebuild:staging": "bash scripts/ios-prebuild.sh staging",
"ios:prebuild:production": "bash scripts/ios-prebuild.sh production",

51
app-expo/scripts/ensure-expo-sqlite-ios.js Normal file
View File

@@ -0,0 +1,51 @@
/**
* expo-sqlite copies vendor/sqlite3/*.c into ios/ during `pod install` (see ExpoSQLite.podspec).
* After a fresh `npm install`, Xcode can fail with missing sqlite3.c until pods run.
* This script mirrors that copy so native builds work without an extra manual step.
*/
const fs = require('fs');
const path = require('path');
const appRoot = path.join(__dirname, '..');
const pkgRoot = path.join(appRoot, 'node_modules', 'expo-sqlite');
function readUseLibSQL() {
const propsPath = path.join(appRoot, 'ios', 'Podfile.properties.json');
if (!fs.existsSync(propsPath)) {
return false;
}
try {
const props = JSON.parse(fs.readFileSync(propsPath, 'utf8'));
return props['expo.sqlite.useLibSQL'] === 'true';
} catch {
return false;
}
}
function main() {
if (!fs.existsSync(pkgRoot)) {
return;
}
if (readUseLibSQL()) {
return;
}
const vendorDir = path.join(pkgRoot, 'vendor', 'sqlite3');
const iosDir = path.join(pkgRoot, 'ios');
for (const file of ['sqlite3.c', 'sqlite3.h']) {
const src = path.join(vendorDir, file);
const dest = path.join(iosDir, file);
if (!fs.existsSync(src)) {
console.warn(`[ensure-expo-sqlite-ios] missing vendor file: ${src}`);
continue;
}
if (fs.existsSync(dest)) {
continue;
}
fs.mkdirSync(iosDir, { recursive: true });
fs.copyFileSync(src, dest);
console.log(`[ensure-expo-sqlite-ios] copied ${file} -> ios/`);
}
}
main();

4
app-expo/scripts/ios-prebuild.sh
View File

@@ -47,6 +47,10 @@ done
echo "==> expo prebuild --platform ios --clean"
npx expo prebuild --platform ios --clean
echo "==> ensure expo-sqlite native sources + pod install"
node scripts/ensure-expo-sqlite-ios.js
(cd ios && pod install)
shopt -s nullglob
WORKSPACES=(ios/*.xcworkspace)
shopt -u nullglob

4
app-expo/src/features/voice/recorder.ts
View File

@@ -18,8 +18,8 @@ type StatusListener = (status: RecorderStatus) => void;
type RecordingCompleteListener = (uri: string, durationMs: number) => void;
/**
* Tencent SentenceRecognition is currently called with `EngSerViceType=16k_zh`
* and `VoiceFormat=m4a`, so record speech in that shape directly instead of
* Tencent SentenceRecognition uses `EngSerViceType=16k_zh_large` and
* `VoiceFormat=m4a`, so record speech in that shape directly instead of
* relying on Expo's default 44.1 kHz stereo preset.
*/
export const VOICE_RECORDING_OPTIONS: RecordingOptions = {

2
app-expo/tests/features/voice/recorder.test.ts
View File

@@ -18,7 +18,7 @@ import {
} from '@/features/voice/recorder';
describe('buildVoiceRecordingOptions', () => {
test('uses 16 kHz mono m4a recording options for Tencent-compatible speech capture', () => {
test('uses 16 kHz mono m4a recording options for Tencent 16k_zh_large capture', () => {
const passedOptions = buildVoiceRecordingOptions();
expect(passedOptions).toEqual(