ESPnet Speech Translation Demonstration

Open In Colab

See also

Author: Shigeki Karita

Install

It takes around 3 minutes. Please keep waiting for a while.

[1]:

# OS setup
!cat /etc/os-release
!apt-get install -qq bc tree sox

# espnet and moses setup
!git clone -q https://github.com/ShigekiKarita/espnet.git
!pip install -q torch==1.1
!cd espnet; git checkout c0466d9a356c1a33f671a546426d7bc33b5b17e8; pip install -q -e .
!cd espnet/tools/; make moses.done

# download pre-compiled warp-ctc and kaldi tools
!espnet/utils/download_from_google_drive.sh \
    "https://drive.google.com/open?id=13Y4tSygc8WtqzvAVGK_vRV9GlV7TRC0w" espnet/tools tar.gz > /dev/null

# make dummy activate
!mkdir -p espnet/tools/venv/bin && touch espnet/tools/venv/bin/activate
!echo "setup done."

NAME="Ubuntu"
VERSION="18.04.3 LTS (Bionic Beaver)"
ID=ubuntu
ID_LIKE=debian
PRETTY_NAME="Ubuntu 18.04.3 LTS"
VERSION_ID="18.04"
HOME_URL="https://www.ubuntu.com/"
SUPPORT_URL="https://help.ubuntu.com/"
BUG_REPORT_URL="https://bugs.launchpad.net/ubuntu/"
PRIVACY_POLICY_URL="https://www.ubuntu.com/legal/terms-and-policies/privacy-policy"
VERSION_CODENAME=bionic
UBUNTU_CODENAME=bionic
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ERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.
ERROR: tensorboardx 2.0 has requirement protobuf>=3.8.0, but you'll have protobuf 3.7.1 which is incompatible.
git clone https://github.com/moses-smt/mosesdecoder.git moses
Cloning into 'moses'...
remote: Enumerating objects: 219, done.
remote: Counting objects: 100% (219/219), done.
remote: Compressing objects: 100% (129/129), done.
remote: Total 147514 (delta 132), reused 135 (delta 84), pack-reused 147295
Receiving objects: 100% (147514/147514), 129.75 MiB | 22.22 MiB/s, done.
Resolving deltas: 100% (113966/113966), done.
touch moses.done
--2020-02-01 06:23:17--  https://drive.google.com/uc?export=download&id=13Y4tSygc8WtqzvAVGK_vRV9GlV7TRC0w
Resolving drive.google.com (drive.google.com)... 172.217.212.113, 172.217.212.100, 172.217.212.139, ...
Connecting to drive.google.com (drive.google.com)|172.217.212.113|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: unspecified [text/html]
Saving to: ‘espnet/tools/gX5bJU.tar.gz’

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2020-02-01 06:23:17 (50.9 MB/s) - ‘espnet/tools/gX5bJU.tar.gz’ saved [3295]


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100  662M    0  662M    0     0  62.8M      0 --:--:--  0:00:10 --:--:-- 72.3M
setup done.

Spanish speech -> English text translation

This audio says “yo soy José.”

[ ]:

from IPython.display import display, Audio
display(Audio("/content/espnet/test_utils/st_test.wav", rate=16000))

Let’s translate this into English text by our pretrained Transformer ST model trained on the Fisher-CALLHOME Spanish dataset.

[2]:

# move on the recipe directory
import os
os.chdir("/content/espnet/egs/fisher_callhome_spanish/st1")

!../../../utils/translate_wav.sh --models fisher_callhome_spanish.transformer.v1.es-en ../../../test_utils/st_test.wav | tee /content/translated.txt

--2020-02-01 06:27:27--  https://drive.google.com/uc?export=download&id=1hawp5ZLw4_SIHIT3edglxbKIIkPVe8n3
Resolving drive.google.com (drive.google.com)... 108.177.112.101, 108.177.112.138, 108.177.112.113, ...
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HTTP request sent, awaiting response... 200 OK
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conf/tuning/train_pytorch_transformer_bpe_short_long.yaml
conf/tuning/decode_pytorch_transformer_bpe.yaml
conf/specaug.yaml
data/train_sp.en/cmvn.ark
exp/train_sp.en_lc.rm_pytorch_train_pytorch_transformer_bpe_short_long_bpe1000_specaug_asrtrans_mttrans/results/model.val5.avg.best
exp/train_sp.en_lc.rm_pytorch_train_pytorch_transformer_bpe_short_long_bpe1000_specaug_asrtrans_mttrans/results/model.json
Sucessfully downloaded zip file from https://drive.google.com/open?id=1hawp5ZLw4_SIHIT3edglxbKIIkPVe8n3
stage 0: Data preparation
stage 1: Feature Generation
steps/make_fbank_pitch.sh --cmd run.pl --nj 1 --write_utt2num_frames true decode/st_test/data decode/st_test/log decode/st_test/fbank
utils/validate_data_dir.sh: WARNING: you have only one speaker.  This probably a bad idea.
   Search for the word 'bold' in http://kaldi-asr.org/doc/data_prep.html
   for more information.
utils/validate_data_dir.sh: Successfully validated data-directory decode/st_test/data
steps/make_fbank_pitch.sh: [info]: no segments file exists: assuming wav.scp indexed by utterance.
steps/make_fbank_pitch.sh: Succeeded creating filterbank and pitch features for data
/content/espnet/egs/fisher_callhome_spanish/st1/../../../utils/dump.sh --cmd run.pl --nj 1 --do_delta false decode/st_test/data/feats.scp decode/download/fisher_callhome_spanish.transformer.v1.es-en/data/train_sp.en/cmvn.ark decode/st_test/log decode/st_test/dump
stage 2: Json Data Preparation
/content/espnet/egs/fisher_callhome_spanish/st1/../../../utils/data2json.sh --feat decode/st_test/dump/feats.scp decode/st_test/data decode/st_test/dict
/content/espnet/egs/fisher_callhome_spanish/st1/../../../utils/feat_to_shape.sh --cmd run.pl --nj 1 --filetype  --preprocess-conf  --verbose 0 decode/st_test/dump/feats.scp decode/st_test/data/tmp-1PxS1/input_1/shape.scp
sym2int.pl: replacing X with 1
** Replaced 1 instances of OOVs with 1
stage 3: Decoding

Detokenizer Version $Revision: 4134 $
Language: en
Translated text: yes i'm jose

Finished

As seen above, we successfully obtained the result: “Translated text: yes i’m jose”!

English translated text-to-speech synthesis

Now let’s generate an English speech from the translated text using a pretrained ESPnet-TTS model.

[ ]:

!sed -n 's/Translated text://p'  /content/translated.txt | tr '[:lower:]' '[:upper:]' | tee /content/translated_sed.txt
!../../../utils/synth_wav.sh /content/translated_sed.txt

 YES I'M JOSE
stage 0: Data preparation
/content/espnet/egs/ljspeech/tts1/../../../utils/data2json.sh --trans_type char decode/translated_sed/data decode/download/ljspeech.transformer.v1/data/lang_1char/train_no_dev_units.txt
stage 2: Decoding
stage 3: Synthesis with Griffin-Lim
apply-cmvn --norm-vars=true --reverse=true decode/download/ljspeech.transformer.v1/data/train_no_dev/cmvn.ark scp:decode/translated_sed/outputs/feats.scp ark,scp:decode/translated_sed/outputs_denorm/feats.ark,decode/translated_sed/outputs_denorm/feats.scp
LOG (apply-cmvn[5.5.428~1-29b3]:main():apply-cmvn.cc:159) Applied cepstral mean and variance normalization to 1 utterances, errors on 0
/content/espnet/egs/ljspeech/tts1/../../../utils/convert_fbank.sh --nj 1 --cmd run.pl --fs 22050 --fmax  --fmin  --n_fft 1024 --n_shift 256 --win_length  --n_mels 80 --iters 64 decode/translated_sed/outputs_denorm decode/translated_sed/log decode/translated_sed/wav
Succeeded creating wav for outputs_denorm

Synthesized wav: decode/translated_sed/wav/translated_sed.wav

Finished
stage 4: Synthesis with Neural Vocoder
2020-01-31 05:38:17,532 (decode:95) INFO: The feature loaded from decode/translated_sed/outputs/feats.scp.
2020-01-31 05:38:21,674 (decode:110) INFO: Loaded model parameters from decode/download/ljspeech.parallel_wavegan.v1/ljspeech.parallel_wavegan.v1/checkpoint-400000steps.pkl.
[decode]: 0it [00:00, ?it/s]THCudaCheck FAIL file=/pytorch/aten/src/THC/THCGeneral.cpp line=383 error=11 : invalid argument
[decode]: 1it [00:00, 13.43it/s, RTF=0.0581]
2020-01-31 05:38:21,750 (decode:134) INFO: Finished generation of 1 utterances (RTF = 0.058).

Synthesized wav: decode/translated_sed/wav_wnv/translated_sed.wav

Finished

[ ]:

import matplotlib.pyplot as plt
import kaldiio
fbank = next(iter(kaldiio.load_scp("decode/translated_sed/outputs/feats.scp").values()))
plt.matshow(fbank.T)

<matplotlib.image.AxesImage at 0x7f7b3ac12e48>
../_images/notebook_st_demo_10_1.png 
[ ]:

from IPython.display import display, Audio
display(Audio("decode/translated_sed/wav_wnv/translated_sed_gen.wav"))

Successfully, it says “Yes I’m Jose”! For more TTS demo, visit https://colab.research.google.com/github/espnet/notebook/blob/master/tts_realtime_demo.ipynb

Check decoding log

After the translation, you will find <decode_dir>/<wav name>/result.json for details;

[ ]:

!cat decode/st_test/result.json

{
    "utts": {
        "st_test": {
            "output": [
                {
                    "name": "target1[1]",
                    "rec_text": "▁yes▁i▁&apos;m▁jose<eos>",
                    "rec_token": "▁yes ▁i ▁ &apos; m ▁j ose <eos>",
                    "rec_tokenid": "991 638 371 2 219 656 256 999",
                    "score": 1.0691701889038088,
                    "shape": [
                        1,
                        3
                    ],
                    "text": "X",
                    "token": "X",
                    "tokenid": "1"
                }
            ],
            "utt2spk": "X"
        }
    }
}

and <decode_dir>/<wav name>/log/decode.log for runtime log;

[25]:

!cat decode/st_test/log/decode.log

# st_trans.py --config decode/download/fisher_callhome_spanish.transformer.v1.es-en/conf/tuning/decode_pytorch_transformer_bpe.yaml --ngpu 0 --backend pytorch --debugmode 1 --verbose 1 --trans-json decode/st_test/dump/data.json --result-label decode/st_test/result.json --model decode/download/fisher_callhome_spanish.transformer.v1.es-en/exp/train_sp.en_lc.rm_pytorch_train_pytorch_transformer_bpe_short_long_bpe1000_specaug_asrtrans_mttrans/results/model.val5.avg.best --api v2
# Started at Sat Feb  1 06:27:34 UTC 2020
#
2020-02-01 06:27:34,638 (st_trans:126) INFO: python path = /env/python
2020-02-01 06:27:34,640 (st_trans:131) INFO: set random seed = 1
2020-02-01 06:27:34,640 (st_trans:139) INFO: backend = pytorch
2020-02-01 06:27:40,269 (deterministic_utils:24) INFO: torch type check is disabled
2020-02-01 06:27:40,269 (asr_utils:439) INFO: reading a config file from decode/download/fisher_callhome_spanish.transformer.v1.es-en/exp/train_sp.en_lc.rm_pytorch_train_pytorch_transformer_bpe_short_long_bpe1000_specaug_asrtrans_mttrans/results/model.json
2020-02-01 06:27:40,270 (asr_init:128) WARNING: reading model parameters from decode/download/fisher_callhome_spanish.transformer.v1.es-en/exp/train_sp.en_lc.rm_pytorch_train_pytorch_transformer_bpe_short_long_bpe1000_specaug_asrtrans_mttrans/results/model.val5.avg.best
/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:46: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.
  warnings.warn(warning.format(ret))
2020-02-01 06:27:41,093 (io_utils:59) WARNING: [Experimental feature] Some preprocessing will be done for the mini-batch creation using Transformation(
    0: TimeWarp(max_time_warp=5, inplace=True, mode=PIL)
    1: FreqMask(F=30, n_mask=2, replace_with_zero=False, inplace=True)
    2: TimeMask(T=40, n_mask=2, replace_with_zero=False, inplace=True))
2020-02-01 06:27:41,093 (st:449) INFO: (1/1) decoding st_test
2020-02-01 06:27:41,094 (e2e_st_transformer:338) INFO: <sos> index: 999
2020-02-01 06:27:41,095 (e2e_st_transformer:339) INFO: <sos> mark: <eos>
2020-02-01 06:27:41,175 (e2e_st_transformer:344) INFO: input lengths: 48
2020-02-01 06:27:41,175 (e2e_st_transformer:357) INFO: max output length: 14
2020-02-01 06:27:41,175 (e2e_st_transformer:358) INFO: min output length: 0
2020-02-01 06:27:42,643 (e2e_st_transformer:421) INFO: adding <eos> in the last postion in the loop
2020-02-01 06:27:42,644 (e2e_st_transformer:451) INFO: no hypothesis. Finish decoding.
2020-02-01 06:27:42,645 (e2e_st_transformer:472) INFO: total log probability: 1.0691701889038088
2020-02-01 06:27:42,645 (e2e_st_transformer:473) INFO: normalized log probability: 0.11879668765597876
2020-02-01 06:27:42,645 (asr_utils:626) INFO: groundtruth: X
2020-02-01 06:27:42,645 (asr_utils:627) INFO: prediction : ▁yes▁i▁&apos;m▁jose<eos>
# Accounting: time=9 threads=1
# Ended (code 0) at Sat Feb  1 06:27:43 UTC 2020, elapsed time 9 seconds

Let’s calculate real-time factor (RTF) of the ST decoding from the decode.log

[54]:

from dateutil import parser
from subprocess import PIPE, run

# calc input duration (seconds)
input_sec = float(run(["soxi", "-D", "/content/espnet/test_utils/st_test.wav"], stdout=PIPE).stdout)

# calc NN decoding time
with open("decode/st_test/log/decode.log", "r") as f:
  times = [parser.parse(x.split("(")[0]) for x in f if "e2e_st_transformer" in x]
decode_sec = (times[-1] - times[0]).total_seconds()

# get real-time factor (RTF)
print("Input duration:\t", input_sec, "sec")
print("NN decoding:\t", decode_sec, "sec")
print("Real-time factor:\t", decode_sec / input_sec)

Input duration:  2.0 sec
NN decoding:     1.551 sec
Real-time factor:        0.7755

As you can see above, ESPnet-ST can translate speech faster than the input (it should be RTF < 1.0).

Training ST models from scratch

We provide Kaldi-style recipes for ST as well as ASR and TTS as all-in-one bash script run.sh:

[ ]:

!cd /content/espnet/egs/must_c/st1/ && ./run.sh --must-c /content

stage -1: Data Download
local/download_and_untar.sh: downloading data from https://drive.google.com/open?id=1Mf2il_VelDIJMSio0bq7I8M9fSs-X4Ie.  This may take some time, please be patient.
--2020-01-31 06:29:10--  https://drive.google.com/uc?export=download&id=1Mf2il_VelDIJMSio0bq7I8M9fSs-X4Ie
Resolving drive.google.com (drive.google.com)... 172.217.214.102, 172.217.214.138, 172.217.214.100, ...
Connecting to drive.google.com (drive.google.com)|172.217.214.102|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: unspecified [text/html]
Saving to: '/content/WGgpBx.tar.gz'

/content/WGgpBx.tar     [ <=>                ]   3.18K  --.-KB/s    in 0s

2020-01-31 06:29:11 (38.8 MB/s) - '/content/WGgpBx.tar.gz' saved [3259]


gzip: stdin: not in gzip format
tar: Child returned status 1
tar: Error is not recoverable: exiting now
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100  3259    0  3259    0     0  16886      0 --:--:-- --:--:-- --:--:-- 16886
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100   388    0   388    0     0   2000      0 --:--:-- --:--:-- --:--:--  1989
100 16.7G    0 16.7G    0     0  40.9M      0 --:--:--  0:06:59 --:--:-- 40.4M^C

However, it takes too much time to finish downloading the dataset. So we cancel the cell above.

Details of ESPnet tools

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!../../../utils/translate_wav.sh --help

Usage:
    ../../../utils/translate_wav.sh [options] <wav_file>

Options:
    --ngpu <ngpu>                   # Number of GPUs (Default: 0)
    --decode_dir <directory_name>   # Name of directory to store decoding temporary data
    --models <model_name>           # Model name (e.g. tedlium2.transformer.v1)
    --cmvn <path>                   # Location of cmvn.ark
    --trans_model <path>            # Location of E2E model
    --decode_config <path>          # Location of configuration file
    --api <api_version>             # API version (v1 or v2)

Example:
    # Record audio from microphone input as example.wav
    rec -c 1 -r 16000 example.wav trim 0 5

    # Decode using model name
    ../../../utils/translate_wav.sh --models must_c.transformer.v1.en-fr example.wav

    # Decode using model file
    ../../../utils/translate_wav.sh --cmvn cmvn.ark --trans_model model.acc.best --decode_config conf/decode.yaml example.wav

    # Decode with GPU (require batchsize > 0 in configuration file)
    ../../../utils/translate_wav.sh --ngpu 1 example.wav

Available models:
    - must_c.transformer.v1.en-fr
    - fisher_callhome_spanish.transformer.v1.es-en

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!../../../utils/synth_wav.sh --help

Usage:
    $ ../../../utils/synth_wav.sh <text>

Example:
    # make text file and then generate it
    echo "This is a demonstration of text to speech." > example.txt
    ../../../utils/synth_wav.sh example.txt

    # you can specify the pretrained models
    ../../../utils/synth_wav.sh --models ljspeech.transformer.v3 example.txt

    # also you can specify vocoder model
    ../../../utils/synth_wav.sh --vocoder_models ljspeech.wavenet.mol.v2 --stop_stage 4 example.txt

Available models:
    - ljspeech.tacotron2.v1
    - ljspeech.tacotron2.v2
    - ljspeech.tacotron2.v3
    - ljspeech.transformer.v1
    - ljspeech.transformer.v2
    - ljspeech.transformer.v3
    - ljspeech.fastspeech.v1
    - ljspeech.fastspeech.v2
    - ljspeech.fastspeech.v3
    - libritts.tacotron2.v1
    - libritts.transformer.v1
    - jsut.transformer.v1
    - jsut.tacotron2.v1
    - csmsc.transformer.v1
    - csmsc.fastspeech.v3

Available vocoder models:
    - ljspeech.wavenet.softmax.ns.v1
    - ljspeech.wavenet.mol.v1
    - ljspeech.parallel_wavegan.v1
    - libritts.wavenet.mol.v1
    - jsut.wavenet.mol.v1
    - jsut.parallel_wavegan.v1
    - csmsc.wavenet.mol.v1
    - csmsc.parallel_wavegan.v1

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