MycroftAI
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mimic2
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Revert "Added dependency for analyze.py running as docker." 

This reverts commit 4ce7f69455.
pull/45/head
Thorsten Mueller 2020-02-21 09:32:25 +01:00
parent 93cb8ada53
commit edeadefe75
4 changed files with 5 additions and 4 deletions
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3
README.md
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@ -212,12 +212,15 @@ Example
python analyze.py --train_file_path=~/tacotron/training/train.txt --save_to=~tacotron/visuals --cmu_dict_path=~/cmudict-0.7b python analyze.py --train_file_path=~/tacotron/training/train.txt --save_to=~tacotron/visuals --cmu_dict_path=~/cmudict-0.7b
``` ```
<<<<<<< HEAD
If using Docker, you need to export your DISPLAY variable when starting the container to successfully create the image files. Also ensure that X server connections are enabled on your host system. If using Docker, you need to export your DISPLAY variable when starting the container to successfully create the image files. Also ensure that X server connections are enabled on your host system.
``` ```
docker run -it -p 3000:3000 -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix mycroft/mimic2:cpu docker run -it -p 3000:3000 -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix mycroft/mimic2:cpu
``` ```
=======
>>>>>>> parent of 4ce7f69... Added dependency for analyze.py running as docker.
cmu_dict_path is optional if you'd like to visualize the distribution of the phonemes. cmu_dict_path is optional if you'd like to visualize the distribution of the phonemes.
analyze.py outputs 6 different plots. analyze.py outputs 6 different plots.

2
cpu.Dockerfile
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@ -3,8 +3,6 @@ FROM tensorflow/tensorflow:1.8.0-py3
RUN mkdir /root/mimic2 RUN mkdir /root/mimic2
COPY . /root/mimic2 COPY . /root/mimic2
WORKDIR /root/mimic2 WORKDIR /root/mimic2
RUN apt-get update
RUN apt-get install -y python3-tk
RUN pip install --no-cache-dir -r requirements.txt RUN pip install --no-cache-dir -r requirements.txt
ENTRYPOINT [ "/bin/bash" ] ENTRYPOINT [ "/bin/bash" ]

2
datasets/createljspeech.py
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@ -30,7 +30,7 @@ def main():
# Create new metadata.csv for ljspeech # Create new metadata.csv for ljspeech
metadata = open(os.path.join(dir_base_ljspeech,"metadata.csv"),mode="w", encoding="utf8") metadata = open(os.path.join(dir_base_ljspeech,"metadata.csv"),mode="w", encoding="utf8")
for row in c.execute('SELECT audio_id, prompt, lower(prompt) FROM audiomodel ORDER BY length(prompt)'): for row in c.execute('SELECT DISTINCT audio_id, prompt, lower(prompt) FROM audiomodel ORDER BY length(prompt)'):
audio_file_source = os.path.join(dir_base_mrs,"backend","audio_files", uid, row[0] + ".wav") audio_file_source = os.path.join(dir_base_mrs,"backend","audio_files", uid, row[0] + ".wav")
if os.path.isfile(audio_file_source): if os.path.isfile(audio_file_source):
metadata.write(row[0] + "|" + row[1] + "|" + row[2] + "\n") metadata.write(row[0] + "|" + row[1] + "|" + row[2] + "\n")

2
datasets/mrs.py
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@ -42,7 +42,7 @@ def build_from_path(in_dir, out_dir, num_workers=1, tqdm=lambda x: x):
wav_dir = os.path.join(in_dir,"backend","audio_files",uid) wav_dir = os.path.join(in_dir,"backend","audio_files",uid)
print("Search for wav files in " + wav_dir) print("Search for wav files in " + wav_dir)
for row in c.execute('SELECT audio_id, lower(prompt) FROM audiomodel ORDER BY length(prompt)'): for row in c.execute('SELECT DISTINCT audio_id, lower(prompt) FROM audiomodel ORDER BY length(prompt)'):
wav_path = os.path.join(wav_dir, '%s.wav' % row[0]) wav_path = os.path.join(wav_dir, '%s.wav' % row[0])
if os.path.isfile(wav_path): if os.path.isfile(wav_path):
text = row[1] text = row[1]