From 9a45a75ae5ea70c2b55a962f5e4812521910014d Mon Sep 17 00:00:00 2001
From: Hui Zhang
Date: Fri, 29 Oct 2021 08:52:49 +0000
Subject: [PATCH] format code
---
README.md | 18 +++++++++---------
deepspeech/exps/deepspeech2/model.py | 1 -
parakeet/data/batch.py | 4 ++--
parakeet/exps/tacotron2/ljspeech.py | 9 +++------
4 files changed, 14 insertions(+), 18 deletions(-)
diff --git a/README.md b/README.md
index 7060a655..8a83ac61 100644
--- a/README.md
+++ b/README.md
@@ -9,20 +9,20 @@ English | [简体中文](README_ch.md)
-
+
------------------------------------------------------------------------------------
![License](https://img.shields.io/badge/license-Apache%202-red.svg)
![python version](https://img.shields.io/badge/python-3.7+-orange.svg)
![support os](https://img.shields.io/badge/os-linux-yellow.svg)
@@ -31,7 +31,7 @@ how they can use it
Via the easy-to-use, efficient, flexible and scalable implementation, our vision is to empower both industrial application and academic research, including training, inference & testing modules, and deployment process. To be more specific, this toolkit features at:
- **Fast and Light-weight**: we provide high-speed and ultra-lightweight models that are convenient for industrial deployment.
- **Rule-based Chinese frontend**: our frontend contains Text Normalization (TN) and Grapheme-to-Phoneme (G2P, including Polyphone and Tone Sandhi). Moreover, we use self-defined linguistic rules to adapt Chinese context.
-- **Varieties of Functions that Vitalize both Industrial and Academia**:
+- **Varieties of Functions that Vitalize both Industrial and Academia**:
- *Implementation of critical audio tasks*: this toolkit contains audio functions like Speech Translation (ST), Automatic Speech Recognition (ASR), Text-To-Speech Synthesis (TTS), Voice Cloning(VC), Punctuation Restoration, etc.
- *Integration of mainstream models and datasets*: the toolkit implements modules that participate in the whole pipeline of the speech tasks, and uses mainstream datasets like LibriSpeech, LJSpeech, AIShell, CSMSC, etc. See also [model lists](#models-list) for more details.
- *Cross-domain application*: as an extension of the application of traditional audio tasks, we combine the aforementioned tasks with other fields like NLP.
@@ -70,7 +70,7 @@ If you want to set up PaddleSpeech in other environment, please see the [ASR ins
## Quick Start
> Note: the current links to `English ASR` and `English TTS` are not valid.
-Just a quick test of our functions: [English ASR](link/hubdetail?name=deepspeech2_aishell&en_category=AutomaticSpeechRecognition) and [English TTS](link/hubdetail?name=fastspeech2_baker&en_category=TextToSpeech) by typing message or upload your own audio file.
+Just a quick test of our functions: [English ASR](link/hubdetail?name=deepspeech2_aishell&en_category=AutomaticSpeechRecognition) and [English TTS](link/hubdetail?name=fastspeech2_baker&en_category=TextToSpeech) by typing message or upload your own audio file.
Developers can have a try of our model with only a few lines of code.
@@ -87,7 +87,7 @@ bash local/test.sh conf/deepspeech2.yaml ckptfile offline
```
For *TTS*, try FastSpeech2 on LJSpeech:
-- Download LJSpeech-1.1 from the [ljspeech official website](https://keithito.com/LJ-Speech-Dataset/), our prepared durations for fastspeech2 [ljspeech_alignment](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz).
+- Download LJSpeech-1.1 from the [ljspeech official website](https://keithito.com/LJ-Speech-Dataset/), our prepared durations for fastspeech2 [ljspeech_alignment](https://paddlespeech.bj.bcebos.com/MFA/LJSpeech-1.1/ljspeech_alignment.tar.gz).
- The pretrained models are seperated into two parts: [fastspeech2_nosil_ljspeech_ckpt](https://paddlespeech.bj.bcebos.com/Parakeet/fastspeech2_nosil_ljspeech_ckpt_0.5.zip) and [pwg_ljspeech_ckpt](https://paddlespeech.bj.bcebos.com/Parakeet/pwg_ljspeech_ckpt_0.5.zip). Please download then unzip to `./model/fastspeech2` and `./model/pwg` respectively.
- Assume your path to the dataset is `~/datasets/LJSpeech-1.1` and `./ljspeech_alignment` accordingly, preprocess your data and then use our pretrained model to synthesize:
```shell
@@ -106,7 +106,7 @@ PaddleSpeech supports a series of most popular models, summarized in [released m
ASR module contains *Acoustic Model* and *Language Model*, with the following details:
> Note: The `Link` should be code path rather than download links.
diff --git a/deepspeech/exps/deepspeech2/model.py b/deepspeech/exps/deepspeech2/model.py
index 6424cfdf..5c010f56 100644
--- a/deepspeech/exps/deepspeech2/model.py
+++ b/deepspeech/exps/deepspeech2/model.py
@@ -189,7 +189,6 @@ class DeepSpeech2Trainer(Trainer):
self.lr_scheduler = lr_scheduler
logger.info("Setup optimizer/lr_scheduler!")
-
def setup_dataloader(self):
config = self.config.clone()
config.defrost()
diff --git a/parakeet/data/batch.py b/parakeet/data/batch.py
index 515074d1..5e7ac399 100644
--- a/parakeet/data/batch.py
+++ b/parakeet/data/batch.py
@@ -53,8 +53,8 @@ def batch_text_id(minibatch, pad_id=0, dtype=np.int64):
peek_example = minibatch[0]
assert len(peek_example.shape) == 1, "text example is an 1D tensor"
- lengths = [example.shape[0] for example in minibatch
- ] # assume (channel, n_samples) or (n_samples, )
+ lengths = [example.shape[0] for example in
+ minibatch] # assume (channel, n_samples) or (n_samples, )
max_len = np.max(lengths)
batch = []
diff --git a/parakeet/exps/tacotron2/ljspeech.py b/parakeet/exps/tacotron2/ljspeech.py
index 20dc29d3..59c855eb 100644
--- a/parakeet/exps/tacotron2/ljspeech.py
+++ b/parakeet/exps/tacotron2/ljspeech.py
@@ -67,19 +67,16 @@ class LJSpeechCollector(object):
# Sort by text_len in descending order
texts = [
- i
- for i, _ in sorted(
+ i for i, _ in sorted(
zip(texts, text_lens), key=lambda x: x[1], reverse=True)
]
mels = [
- i
- for i, _ in sorted(
+ i for i, _ in sorted(
zip(mels, text_lens), key=lambda x: x[1], reverse=True)
]
mel_lens = [
- i
- for i, _ in sorted(
+ i for i, _ in sorted(
zip(mel_lens, text_lens), key=lambda x: x[1], reverse=True)
]