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(简体中文|English)

Speech Server

Introduction

This demo is an implementation of starting the voice service and accessing the service. It can be achieved with a single command using paddlespeech_server and paddlespeech_client or a few lines of code in python.

For service interface definition, please check:

Usage

1. Installation

see installation.

It is recommended to use paddlepaddle 2.4rc or above.

You can choose one way from easy, meduim and hard to install paddlespeech.

If you install in easy mode, you need to prepare the yaml file by yourself, you can refer to the yaml file in the conf directory.

2. Prepare config File

The configuration file can be found in conf/application.yaml . Among them, engine_list indicates the speech engine that will be included in the service to be started, in the format of <speech task>_<engine type>. At present, the speech tasks integrated by the service include: asr (speech recognition), tts (text to sppech) and cls (audio classification). Currently the engine type supports two forms: python and inference (Paddle Inference) Note: If the service can be started normally in the container, but the client access IP is unreachable, you can try to replace the host address in the configuration file with the local IP address.

3. Server Usage

  • Command Line (Recommended)

    # start the service
paddlespeech_server start --config_file ./conf/application.yaml

    Usage:

    paddlespeech_server start --help

    Arguments:

    • config_file: yaml file of the app, defalut: ./conf/application.yaml
    • log_file: log file. Default: ./log/paddlespeech.log

    Output:

    [2022-02-23 11:17:32] [INFO] [server.py:64] Started server process [6384]
INFO:     Waiting for application startup.
[2022-02-23 11:17:32] [INFO] [on.py:26] Waiting for application startup.
INFO:     Application startup complete.
[2022-02-23 11:17:32] [INFO] [on.py:38] Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
[2022-02-23 11:17:32] [INFO] [server.py:204] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)

  • Python API

    from paddlespeech.server.bin.paddlespeech_server import ServerExecutor

server_executor = ServerExecutor()
server_executor(
    config_file="./conf/application.yaml", 
    log_file="./log/paddlespeech.log")

    Output:

    INFO:     Started server process [529]
[2022-02-23 14:57:56] [INFO] [server.py:64] Started server process [529]
INFO:     Waiting for application startup.
[2022-02-23 14:57:56] [INFO] [on.py:26] Waiting for application startup.
INFO:     Application startup complete.
[2022-02-23 14:57:56] [INFO] [on.py:38] Application startup complete.
INFO:     Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)
[2022-02-23 14:57:56] [INFO] [server.py:204] Uvicorn running on http://0.0.0.0:8090 (Press CTRL+C to quit)

4. ASR Client Usage

The input of ASR client demo should be a WAV file(.wav), and the sample rate must be the same as the model.

Here are sample files for this ASR client demo that can be downloaded:

wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav
wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/en.wav

Note: The response time will be slightly longer when using the client for the first time

  • Command Line (Recommended)

    If 127.0.0.1 is not accessible, you need to use the actual service IP address.

    paddlespeech_client asr --server_ip 127.0.0.1 --port 8090 --input ./zh.wav

    Usage:

    paddlespeech_client asr --help

    Arguments:

    • server_ip: server ip. Default: 127.0.0.1
    • port: server port. Default: 8090
    • input(required): Audio file to be recognized.
    • sample_rate: Audio ampling rate, default: 16000.
    • lang: Language. Default: "zh_cn".
    • audio_format: Audio format. Default: "wav".

    Output:

    [2022-08-01 07:54:01,646] [    INFO] - ASR result: 我认为跑步最重要的就是给我带来了身体健康
[2022-08-01 07:54:01,646] [    INFO] - Response time 4.898965 s.

  • Python API

    from paddlespeech.server.bin.paddlespeech_client import ASRClientExecutor

asrclient_executor = ASRClientExecutor()
res = asrclient_executor(
    input="./zh.wav",
    server_ip="127.0.0.1",
    port=8090,
    sample_rate=16000,
    lang="zh_cn",
    audio_format="wav")
print(res)

    Output:

    我认为跑步最重要的就是给我带来了身体健康

5. TTS Client Usage

Note: The response time will be slightly longer when using the client for the first time

  • Command Line (Recommended)

    If 127.0.0.1 is not accessible, you need to use the actual service IP address

    paddlespeech_client tts --server_ip 127.0.0.1 --port 8090 --input "您好，欢迎使用百度飞桨语音合成服务。" --output output.wav

    Usage:

    paddlespeech_client tts --help

    Arguments:

    • server_ip: server ip. Default: 127.0.0.1
    • port: server port. Default: 8090
    • input(required): Input text to generate.
    • spk_id: Speaker id for multi-speaker text to speech. Default: 0
    • speed: Audio speed, the value should be set between 0 and 3. Default: 1.0
    • volume: Audio volume, the value should be set between 0 and 3. Default: 1.0
    • sample_rate: Sampling rate, choice: [0, 8000, 16000], the default is the same as the model. Default: 0
    • output: Output wave filepath. Default: None, which means not to save the audio to the local.

    Output:

    [2022-02-23 15:20:37,875] [    INFO] - Save synthesized audio successfully on output.wav.
[2022-02-23 15:20:37,875] [    INFO] - Audio duration: 3.612500 s.
[2022-02-23 15:20:37,875] [    INFO] - Response time: 0.348050 s.

  • Python API

    from paddlespeech.server.bin.paddlespeech_client import TTSClientExecutor
import json

ttsclient_executor = TTSClientExecutor()
res = ttsclient_executor(
    input="您好，欢迎使用百度飞桨语音合成服务。",
    server_ip="127.0.0.1",
    port=8090,
    spk_id=0,
    speed=1.0,
    volume=1.0,
    sample_rate=0,
    output="./output.wav")

response_dict = res.json()
print(response_dict["message"])
print("Save synthesized audio successfully on %s." % (response_dict['result']['save_path']))
print("Audio duration: %f s." %(response_dict['result']['duration']))

    Output:

    {'description': 'success.'}
Save synthesized audio successfully on ./output.wav.
Audio duration: 3.612500 s.

6. CLS Client Usage

Here are sample files for this CLS Client demo that can be downloaded:

wget -c https://paddlespeech.bj.bcebos.com/PaddleAudio/zh.wav

Note: The response time will be slightly longer when using the client for the first time

  • Command Line (Recommended)

    If 127.0.0.1 is not accessible, you need to use the actual service IP address.

    paddlespeech_client cls --server_ip 127.0.0.1 --port 8090 --input ./zh.wav

    Usage:

    paddlespeech_client cls --help

    Arguments:

    • server_ip: server ip. Default: 127.0.0.1
    • port: server port. Default: 8090
    • input(required): Audio file to be classified.
    • topk: topk scores of classification result.

    Output:

    [2022-03-09 20:44:39,974] [    INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}}
[2022-03-09 20:44:39,975] [    INFO] - Response time 0.104360 s.

  • Python API

    from paddlespeech.server.bin.paddlespeech_client import CLSClientExecutor
import json

clsclient_executor = CLSClientExecutor()
res = clsclient_executor(
    input="./zh.wav",
    server_ip="127.0.0.1",
    port=8090,
    topk=1)
print(res.json())

    Output:

    {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'topk': 1, 'results': [{'class_name': 'Speech', 'prob': 0.9027184844017029}]}}

7. Speaker Verification Client Usage

Here are sample files for this Speaker Verification Client demo that can be downloaded:

wget -c https://paddlespeech.bj.bcebos.com/vector/audio/85236145389.wav
wget -c https://paddlespeech.bj.bcebos.com/vector/audio/123456789.wav

7.1 Extract speaker embedding

Note: The response time will be slightly longer when using the client for the first time

  • Command Line (Recommended)

    If 127.0.0.1 is not accessible, you need to use the actual service IP address.

    paddlespeech_client vector --task spk  --server_ip 127.0.0.1 --port 8090 --input 85236145389.wav

    Usage:

    paddlespeech_client vector --help

    Arguments:

    • server_ip: server ip. Default: 127.0.0.1
    • port: server port. Default: 8090
    • input(required): Input text to generate.
    • task: the task of vector, can be use 'spk' or 'score。Default is 'spk'。
    • enroll: enroll audio
    • test: test audio

    Output:

    [2022-08-01 09:01:22,151] [    INFO] - vector http client start
[2022-08-01 09:01:22,152] [    INFO] - the input audio: 85236145389.wav
[2022-08-01 09:01:22,152] [    INFO] - endpoint: http://127.0.0.1:8090/paddlespeech/vector
[2022-08-01 09:01:27,093] [    INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'vec': [1.4217487573623657, 5.626248836517334, -5.342073440551758, 1.177390217781067, 3.308061122894287, 1.7565997838974, 5.1678876876831055, 10.806346893310547, -3.822679042816162, -5.614130973815918, 2.6238481998443604, -0.8072965741157532, 1.963512659072876, -7.312864780426025, 0.011034967377781868, -9.723127365112305, 0.661963164806366, -6.976816654205322, 10.213465690612793, 7.494767189025879, 2.9105641841888428, 3.894925117492676, 3.7999846935272217, 7.106173992156982, 16.905324935913086, -7.149376392364502, 8.733112335205078, 3.423002004623413, -4.831653118133545, -11.403371810913086, 11.232216835021973, 7.127464771270752, -4.282831192016602, 2.4523589611053467, -5.13075065612793, -18.17765998840332, -2.611666440963745, -11.00034236907959, -6.731431007385254, 1.6564655303955078, 0.7618184685707092, 1.1253058910369873, -2.0838277339935303, 4.725739002227783, -8.782590866088867, -3.5398736000061035, 3.8142387866973877, 5.142062664031982, 2.162053346633911, 4.09642219543457, -6.416221618652344, 12.747454643249512, 1.9429889917373657, -15.152948379516602, 6.417416572570801, 16.097013473510742, -9.716649055480957, -1.9920448064804077, -3.364956855773926, -1.8719490766525269, 11.567351341247559, 3.6978795528411865, 11.258269309997559, 7.442364692687988, 9.183405876159668, 4.528151512145996, -1.2417811155319214, 4.395910263061523, 6.672768592834473, 5.889888763427734, 7.627115249633789, -0.6692016124725342, -11.889703750610352, -9.208883285522461, -7.427401542663574, -3.777655601501465, 6.917237758636475, -9.848749160766602, -2.094479560852051, -5.1351189613342285, 0.49564215540885925, 9.317541122436523, -5.9141845703125, -1.809845209121704, -0.11738205701112747, -7.169270992279053, -1.0578246116638184, -5.721685886383057, -5.117387294769287, 16.137670516967773, -4.473618984222412, 7.66243314743042, -0.5538089871406555, 9.631582260131836, -6.470466613769531, -8.54850959777832, 4.371622085571289, -0.7970349192619324, 4.479003429412842, -2.9758646488189697, 3.2721707820892334, 2.8382749557495117, 5.1345953941345215, -9.19078254699707, -0.5657423138618469, -4.874573230743408, 2.316561460494995, -5.984307289123535, -2.1798791885375977, 0.35541653633117676, -0.3178458511829376, 9.493547439575195, 2.114448070526123, 4.358088493347168, -12.089820861816406, 8.451695442199707, -7.925461769104004, 4.624246120452881, 4.428938388824463, 18.691999435424805, -2.620460033416748, -5.149182319641113, -0.3582168221473694, 8.488557815551758, 4.98148250579834, -9.326834678649902, -2.2544236183166504, 6.64176607131958, 1.2119656801223755, 10.977132797241211, 16.55504035949707, 3.323848247528076, 9.55185317993164, -1.6677050590515137, -0.7953923940658569, -8.605660438537598, -0.4735637903213501, 2.6741855144500732, -5.359188079833984, -2.6673784255981445, 0.6660736799240112, 15.443212509155273, 4.740597724914551, -3.4725306034088135, 11.592561721801758, -2.05450701713562, 1.7361239194869995, -8.26533031463623, -9.304476737976074, 5.406835079193115, -1.5180232524871826, -7.746610641479492, -6.089605331420898, 0.07112561166286469, -0.34904858469963074, -8.649889945983887, -9.998958587646484, -2.5648481845855713, -0.5399898886680603, 2.6018145084381104, -0.31927648186683655, -1.8815231323242188, -2.0721378326416016, -3.4105639457702637, -8.299802780151367, 1.4836379289627075, -15.366002082824707, -8.288193702697754, 3.884773015975952, -3.4876506328582764, 7.362995624542236, 0.4657321572303772, 3.1326000690460205, 12.438883781433105, -1.8337029218673706, 4.532927513122559, 2.726433277130127, 10.145345687866211, -6.521956920623779, 2.8971481323242188, -3.3925881385803223, 5.079156398773193, 7.759725093841553, 4.677562236785889, 5.8457818031311035, 2.4023921489715576, 7.707108974456787, 3.9711389541625977, -6.390035152435303, 6.126871109008789, -3.776031017303467, -11.118141174316406]}}
[2022-08-01 09:01:27,094] [    INFO] - Response time 4.941739 s.

  • Python API

    from paddlespeech.server.bin.paddlespeech_client import VectorClientExecutor
import json

vectorclient_executor = VectorClientExecutor()
res = vectorclient_executor(
    input="85236145389.wav",
    server_ip="127.0.0.1",
    port=8090,
    task="spk")
print(res.json())

    Output:

    {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'vec': [1.4217487573623657, 5.626248836517334, -5.342073440551758, 1.177390217781067, 3.308061122894287, 1.7565997838974, 5.1678876876831055, 10.806346893310547, -3.822679042816162, -5.614130973815918, 2.6238481998443604, -0.8072965741157532, 1.963512659072876, -7.312864780426025, 0.011034967377781868, -9.723127365112305, 0.661963164806366, -6.976816654205322, 10.213465690612793, 7.494767189025879, 2.9105641841888428, 3.894925117492676, 3.7999846935272217, 7.106173992156982, 16.905324935913086, -7.149376392364502, 8.733112335205078, 3.423002004623413, -4.831653118133545, -11.403371810913086, 11.232216835021973, 7.127464771270752, -4.282831192016602, 2.4523589611053467, -5.13075065612793, -18.17765998840332, -2.611666440963745, -11.00034236907959, -6.731431007385254, 1.6564655303955078, 0.7618184685707092, 1.1253058910369873, -2.0838277339935303, 4.725739002227783, -8.782590866088867, -3.5398736000061035, 3.8142387866973877, 5.142062664031982, 2.162053346633911, 4.09642219543457, -6.416221618652344, 12.747454643249512, 1.9429889917373657, -15.152948379516602, 6.417416572570801, 16.097013473510742, -9.716649055480957, -1.9920448064804077, -3.364956855773926, -1.8719490766525269, 11.567351341247559, 3.6978795528411865, 11.258269309997559, 7.442364692687988, 9.183405876159668, 4.528151512145996, -1.2417811155319214, 4.395910263061523, 6.672768592834473, 5.889888763427734, 7.627115249633789, -0.6692016124725342, -11.889703750610352, -9.208883285522461, -7.427401542663574, -3.777655601501465, 6.917237758636475, -9.848749160766602, -2.094479560852051, -5.1351189613342285, 0.49564215540885925, 9.317541122436523, -5.9141845703125, -1.809845209121704, -0.11738205701112747, -7.169270992279053, -1.0578246116638184, -5.721685886383057, -5.117387294769287, 16.137670516967773, -4.473618984222412, 7.66243314743042, -0.5538089871406555, 9.631582260131836, -6.470466613769531, -8.54850959777832, 4.371622085571289, -0.7970349192619324, 4.479003429412842, -2.9758646488189697, 3.2721707820892334, 2.8382749557495117, 5.1345953941345215, -9.19078254699707, -0.5657423138618469, -4.874573230743408, 2.316561460494995, -5.984307289123535, -2.1798791885375977, 0.35541653633117676, -0.3178458511829376, 9.493547439575195, 2.114448070526123, 4.358088493347168, -12.089820861816406, 8.451695442199707, -7.925461769104004, 4.624246120452881, 4.428938388824463, 18.691999435424805, -2.620460033416748, -5.149182319641113, -0.3582168221473694, 8.488557815551758, 4.98148250579834, -9.326834678649902, -2.2544236183166504, 6.64176607131958, 1.2119656801223755, 10.977132797241211, 16.55504035949707, 3.323848247528076, 9.55185317993164, -1.6677050590515137, -0.7953923940658569, -8.605660438537598, -0.4735637903213501, 2.6741855144500732, -5.359188079833984, -2.6673784255981445, 0.6660736799240112, 15.443212509155273, 4.740597724914551, -3.4725306034088135, 11.592561721801758, -2.05450701713562, 1.7361239194869995, -8.26533031463623, -9.304476737976074, 5.406835079193115, -1.5180232524871826, -7.746610641479492, -6.089605331420898, 0.07112561166286469, -0.34904858469963074, -8.649889945983887, -9.998958587646484, -2.5648481845855713, -0.5399898886680603, 2.6018145084381104, -0.31927648186683655, -1.8815231323242188, -2.0721378326416016, -3.4105639457702637, -8.299802780151367, 1.4836379289627075, -15.366002082824707, -8.288193702697754, 3.884773015975952, -3.4876506328582764, 7.362995624542236, 0.4657321572303772, 3.1326000690460205, 12.438883781433105, -1.8337029218673706, 4.532927513122559, 2.726433277130127, 10.145345687866211, -6.521956920623779, 2.8971481323242188, -3.3925881385803223, 5.079156398773193, 7.759725093841553, 4.677562236785889, 5.8457818031311035, 2.4023921489715576, 7.707108974456787, 3.9711389541625977, -6.390035152435303, 6.126871109008789, -3.776031017303467, -11.118141174316406]}}

7.2 Get the score between speaker audio embedding

Note: The response time will be slightly longer when using the client for the first time

  • Command Line (Recommended)

    If 127.0.0.1 is not accessible, you need to use the actual service IP address.

    paddlespeech_client vector --task score  --server_ip 127.0.0.1 --port 8090 --enroll 85236145389.wav --test 123456789.wav

    Usage:

    paddlespeech_client vector --help

    Arguments:

    • server_ip: server ip. Default: 127.0.0.1
    • port: server port. Default: 8090
    • input(required): Input text to generate.
    • task: the task of vector, can be use 'spk' or 'score。If get the score, this must be 'score' parameter.
    • enroll: enroll audio
    • test: test audio

    Output:

    [2022-08-01 09:04:42,275] [    INFO] - vector score http client start
[2022-08-01 09:04:42,275] [    INFO] - enroll audio: 85236145389.wav, test audio: 123456789.wav
[2022-08-01 09:04:42,275] [    INFO] - endpoint: http://127.0.0.1:8090/paddlespeech/vector/score
[2022-08-01 09:04:44,611] [    INFO] - {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'score': 0.4292638897895813}}
[2022-08-01 09:04:44,611] [    INFO] - Response time 2.336258 s.

  • Python API

    from paddlespeech.server.bin.paddlespeech_client import VectorClientExecutor
import json

vectorclient_executor = VectorClientExecutor()
res = vectorclient_executor(
    input=None,
    enroll_audio="85236145389.wav",
    test_audio="123456789.wav",
    server_ip="127.0.0.1",
    port=8090,
    task="score")
print(res.json())

    Output:

    {'success': True, 'code': 200, 'message': {'description': 'success'}, 'result': {'score': 0.4292638897895813}}

8. Punctuation prediction

Note: The response time will be slightly longer when using the client for the first time

  • Command Line (Recommended)

    If 127.0.0.1 is not accessible, you need to use the actual service IP address.

    paddlespeech_client text --server_ip 127.0.0.1 --port 8090 --input "我认为跑步最重要的就是给我带来了身体健康"

    Usage:

    paddlespeech_client text --help

    Arguments:

    • server_ip: server ip. Default: 127.0.0.1
    • port: server port. Default: 8090
    • input(required): Input text to get punctuation.

    Output:

    [2022-05-09 18:19:04,397] [    INFO] - The punc text: 我认为跑步最重要的就是给我带来了身体健康。
[2022-05-09 18:19:04,397] [    INFO] - Response time 0.092407 s.

  • Python API

    from paddlespeech.server.bin.paddlespeech_client import TextClientExecutor

textclient_executor = TextClientExecutor()
res = textclient_executor(
    input="我认为跑步最重要的就是给我带来了身体健康",
    server_ip="127.0.0.1",
    port=8090,)
print(res)

    Output:

    我认为跑步最重要的就是给我带来了身体健康。

Models supported by the service

ASR model

Get all models supported by the ASR service via paddlespeech_server stats --task asr, where static models can be used for paddle inference inference.

TTS model

Get all models supported by the TTS service via paddlespeech_server stats --task tts, where static models can be used for paddle inference inference.

CLS model

Get all models supported by the CLS service via paddlespeech_server stats --task cls, where static models can be used for paddle inference inference.

Vector model

Get all models supported by the TTS service via paddlespeech_server stats --task vector, where static models can be used for paddle inference inference.

Text model

Get all models supported by the CLS service via paddlespeech_server stats --task text, where static models can be used for paddle inference inference.