记录一下 coqui-ai/TTS 启动 http 服务的方式
本帖最后由 HelloWorld 于 2025-4-18 19:10 编辑参考官方教程：https://docs.coqui.ai/en/latest/docker_images.html#start-a-server
持久化运行容器：docker run -d --restart always -p 5002:5002 --entrypoint python3 ghcr.io/coqui-ai/tts-cpu TTS/server/server.py --model_name tts_models/en/ljspeech/vits
运行后直接打开网页测试：http://your_ip:5002/
音频 wav 音频文件链接为：http://your_ip:5002/api/tts?speaker_id=p364&text=nice%20to%20meet%20you
镜像很大，拉取时你要忍一下：
如果你想运行多个 model，需要跑多个容器，然后用不同端口映射去提供服务
有些模型不支持服务器模式，例如 tts_models/en/multi-dataset/tortoise-v2，所以你运行容器，发现无法通过端口访问，那估计是模型不支持
如果你想在 ARM 电脑上测试，需要用下面的方式去仿真 AMD：docker pull ghcr.io/coqui-ai/tts-cpu --platform linux/amd64
docker run --rm -it -p 5002:5002 --platform linux/amd64 ghcr.io/coqui-ai/tts-cpu
以下是我打印出的模型列表，每个版本的镜像可能模型不一样
不推荐用 /fast_pitch 结尾的模型，这种可能是因为追求速度，声音马赛克感很重
对比了多个模型，这个效果最好：tts_models/en/ljspeech/vits
python3 TTS/server/server.py --list_models
Name format: type/language/dataset/model
1: tts_models/multilingual/multi-dataset/xtts_v2
2: tts_models/multilingual/multi-dataset/xtts_v1.1
3: tts_models/multilingual/multi-dataset/your_tts
4: tts_models/multilingual/multi-dataset/bark
5: tts_models/bg/cv/vits
6: tts_models/cs/cv/vits
7: tts_models/da/cv/vits
8: tts_models/et/cv/vits
9: tts_models/ga/cv/vits
10: tts_models/en/ek1/tacotron2
11: tts_models/en/ljspeech/tacotron2-DDC
12: tts_models/en/ljspeech/tacotron2-DDC_ph
13: tts_models/en/ljspeech/glow-tts
14: tts_models/en/ljspeech/speedy-speech
15: tts_models/en/ljspeech/tacotron2-DCA
16: tts_models/en/ljspeech/vits
17: tts_models/en/ljspeech/vits--neon
18: tts_models/en/ljspeech/fast_pitch
19: tts_models/en/ljspeech/overflow
20: tts_models/en/ljspeech/neural_hmm
21: tts_models/en/vctk/vits
22: tts_models/en/vctk/fast_pitch
23: tts_models/en/sam/tacotron-DDC
24: tts_models/en/blizzard2013/capacitron-t2-c50
25: tts_models/en/blizzard2013/capacitron-t2-c150_v2
26: tts_models/en/multi-dataset/tortoise-v2
27: tts_models/en/jenny/jenny
28: tts_models/es/mai/tacotron2-DDC
29: tts_models/es/css10/vits
30: tts_models/fr/mai/tacotron2-DDC
31: tts_models/fr/css10/vits
32: tts_models/uk/mai/glow-tts
33: tts_models/uk/mai/vits
34: tts_models/zh-CN/baker/tacotron2-DDC-GST
35: tts_models/nl/mai/tacotron2-DDC
36: tts_models/nl/css10/vits
37: tts_models/de/thorsten/tacotron2-DCA
38: tts_models/de/thorsten/vits
39: tts_models/de/thorsten/tacotron2-DDC
40: tts_models/de/css10/vits-neon
41: tts_models/ja/kokoro/tacotron2-DDC
42: tts_models/tr/common-voice/glow-tts
43: tts_models/it/mai_female/glow-tts
44: tts_models/it/mai_female/vits
45: tts_models/it/mai_male/glow-tts
46: tts_models/it/mai_male/vits
47: tts_models/ewe/openbible/vits
48: tts_models/hau/openbible/vits
49: tts_models/lin/openbible/vits
50: tts_models/tw_akuapem/openbible/vits
51: tts_models/tw_asante/openbible/vits
52: tts_models/yor/openbible/vits
53: tts_models/hu/css10/vits
54: tts_models/el/cv/vits
55: tts_models/fi/css10/vits
56: tts_models/hr/cv/vits
57: tts_models/lt/cv/vits
58: tts_models/lv/cv/vits
59: tts_models/mt/cv/vits
60: tts_models/pl/mai_female/vits
61: tts_models/pt/cv/vits
62: tts_models/ro/cv/vits
63: tts_models/sk/cv/vits
64: tts_models/sl/cv/vits
65: tts_models/sv/cv/vits
66: tts_models/ca/custom/vits
67: tts_models/fa/custom/glow-tts
68: tts_models/bn/custom/vits-male
69: tts_models/bn/custom/vits-female
70: tts_models/be/common-voice/glow-tts
Name format: type/language/dataset/model
1: vocoder_models/universal/libri-tts/wavegrad
2: vocoder_models/universal/libri-tts/fullband-melgan
3: vocoder_models/en/ek1/wavegrad
4: vocoder_models/en/ljspeech/multiband-melgan
5: vocoder_models/en/ljspeech/hifigan_v2
6: vocoder_models/en/ljspeech/univnet
7: vocoder_models/en/blizzard2013/hifigan_v2
8: vocoder_models/en/vctk/hifigan_v2
9: vocoder_models/en/sam/hifigan_v2
10: vocoder_models/nl/mai/parallel-wavegan
11: vocoder_models/de/thorsten/wavegrad
12: vocoder_models/de/thorsten/fullband-melgan
13: vocoder_models/de/thorsten/hifigan_v1
14: vocoder_models/ja/kokoro/hifigan_v1
15: vocoder_models/uk/mai/multiband-melgan
16: vocoder_models/tr/common-voice/hifigan
17: vocoder_models/be/common-voice/hifigan
Name format: type/language/dataset/model
1: voice_conversion_models/multilingual/vctk/freevc24
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