yumoqing
df8aafe1d8
- POST /v1/audio/speech (TTS): MiniMax Speech 2.6 Turbo/HD, 2.5 HD, F5-TTS local - POST /v1/audio/transcriptions (ASR): qwen3-asr-flash, Nvidia parakeet - Add comprehensive docs for both endpoints in API.md - Update load_path.py RBAC (logined + customer roles)
75 lines
2.1 KiB
Plaintext
75 lines
2.1 KiB
Plaintext
# OpenAI-compatible Text-to-Speech API
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# POST /v1/audio/speech
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# Required params: model, catelogid, prompt (text to synthesize)
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# Optional params: speaker (voice_id), speed, emotion
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#
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# Example request:
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# {
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# "model": "speech-2.6-turbo",
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# "catelogid": "tts",
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# "prompt": "你好，欢迎使用语音合成服务",
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# "speaker": "female-tianmei",
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# "speed": 1.0,
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# "emotion": "happy"
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# }
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#
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# Response (stream, hex audio chunks):
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# {
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# "status": "SUCCEEDED",
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# "audio": "base64_encoded_audio_data"
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# }
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userid = await get_user()
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userorgid = await get_userorgid()
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if userid is None:
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debug('need login')
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return openai_403()
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# Validate required parameters
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if not params_kw.model:
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d = return_error('Missing required parameter: model')
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return json_response(d, status=400)
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if not params_kw.catelogid:
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d = return_error('Missing required parameter: catelogid')
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return json_response(d, status=400)
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if not params_kw.prompt:
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d = return_error('Missing required parameter: prompt (text to synthesize)')
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return json_response(d, status=400)
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lctype = params_kw.catelogid
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env = request._run_ns
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async with get_sor_context(env, 'llmage') as sor:
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# Look up llm by model name and catalog type through llm_api_map
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sql = """select distinct a.* from llm a
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join llm_api_map m on a.id = m.llmid
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join llmcatelog b on m.llmcatelogid = b.id
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where (b.id = ${lctype}$ OR b.name = ${lctype}$)
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and a.model=${model}$
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and a.status = 'published'"""
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recs = await sor.sqlExe(sql, {
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'lctype': lctype,
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'model': params_kw.model
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})
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if len(recs) == 0:
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debug(f'{params_kw.model=} not found for catalog {lctype}')
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return openai_400()
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params_kw.llmid = recs[0].id
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debug(f'{params_kw.llmid=}')
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# Check balance
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f = await checkCustomerBalance(params_kw.llmid, userid, userorgid)
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if not f:
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debug(f'{userid=} balance not enough')
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return openai_429()
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# Generate task ID and attach to params
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if not params_kw.transno:
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params_kw.transno = getID()
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# Call inference (TTS can be stream or sync depending on model)
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return await inference(request, env=env)
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