Update model

README.md

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+---
+tags:
+- tensorflowtts
+- audio
+- text-to-speech
+- text-to-mel
+language: fr
+license: apache-2.0
+datasets:
+- synpaflex
+widget:
+- text: "Oh, je voudrais tant que tu te souviennes Des jours heureux quand nous étions amis"
+---
+
+# Tacotron 2 with Guided Attention trained on Synpaflex (Fr)
+This repository provides a pretrained [Tacotron2](https://arxiv.org/abs/1712.05884) trained with [Guided Attention](https://arxiv.org/abs/1710.08969) on Synpaflex dataset (Fr). For a detail of the model, we encourage you to read more about
+[TensorFlowTTS](https://github.com/TensorSpeech/TensorFlowTTS). 
+
+
+## Install TensorFlowTTS
+First of all, please install TensorFlowTTS with the following command:
+```
+pip install TensorFlowTTS
+```
+
+### Converting your Text to Mel Spectrogram
+```python
+import numpy as np
+import soundfile as sf
+import yaml
+
+import tensorflow as tf
+
+from tensorflow_tts.inference import AutoProcessor
+from tensorflow_tts.inference import TFAutoModel
+
+processor = AutoProcessor.from_pretrained("tensorspeech/tts-tacotron2-synpaflex-fr")
+tacotron2 = TFAutoModel.from_pretrained("tensorspeech/tts-tacotron2-synpaflex-fr")
+
+text = "Oh, je voudrais tant que tu te souviennes Des jours heureux quand nous étions amis"
+
+input_ids = processor.text_to_sequence(text)
+
+decoder_output, mel_outputs, stop_token_prediction, alignment_history = tacotron2.inference(
+    input_ids=tf.expand_dims(tf.convert_to_tensor(input_ids, dtype=tf.int32), 0),
+    input_lengths=tf.convert_to_tensor([len(input_ids)], tf.int32),
+    speaker_ids=tf.convert_to_tensor([0], dtype=tf.int32),
+)
+
+```
+
+#### Referencing Tacotron 2
+```
+@article{DBLP:journals/corr/abs-1712-05884,
+  author    = {Jonathan Shen and
+               Ruoming Pang and
+               Ron J. Weiss and
+               Mike Schuster and
+               Navdeep Jaitly and
+               Zongheng Yang and
+               Zhifeng Chen and
+               Yu Zhang and
+               Yuxuan Wang and
+               R. J. Skerry{-}Ryan and
+               Rif A. Saurous and
+               Yannis Agiomyrgiannakis and
+               Yonghui Wu},
+  title     = {Natural {TTS} Synthesis by Conditioning WaveNet on Mel Spectrogram
+               Predictions},
+  journal   = {CoRR},
+  volume    = {abs/1712.05884},
+  year      = {2017},
+  url       = {http://arxiv.org/abs/1712.05884},
+  archivePrefix = {arXiv},
+  eprint    = {1712.05884},
+  timestamp = {Thu, 28 Nov 2019 08:59:52 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-1712-05884.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```
+
+#### Referencing TensorFlowTTS
+```
+@misc{TFTTS,
+    author = {Minh Nguyen, Alejandro Miguel Velasquez, Erogol, Kuan Chen, Dawid Kobus, Takuya Ebata, 
+    Trinh Le and Yunchao He},
+    title = {TensorflowTTS},
+    year = {2020},
+    publisher = {GitHub},
+    journal = {GitHub repository},
+    howpublished = {\\url{https://github.com/TensorSpeech/TensorFlowTTS}},
+  }
+```

config.yml

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+# This is the hyperparameter configuration file for Tacotron2 v1.
+# Please make sure this is adjusted for the synpaflex dataset. If you want to
+# apply to the other dataset, you might need to carefully change some parameters.
+# This configuration performs 200k iters but 65k iters is enough to get a good models.
+
+###########################################################
+#                FEATURE EXTRACTION SETTING               #
+###########################################################
+hop_size: 256            # Hop size.
+format: "npy"
+
+
+###########################################################
+#              NETWORK ARCHITECTURE SETTING               #
+###########################################################
+model_type: "tacotron2"
+
+tacotron2_params:
+    dataset: synpaflex
+    embedding_hidden_size: 512
+    initializer_range: 0.02
+    embedding_dropout_prob: 0.1
+    n_speakers: 1
+    n_conv_encoder: 5
+    encoder_conv_filters: 512
+    encoder_conv_kernel_sizes: 5
+    encoder_conv_activation: 'relu'
+    encoder_conv_dropout_rate: 0.5
+    encoder_lstm_units: 256
+    n_prenet_layers: 2
+    prenet_units: 256
+    prenet_activation: 'relu'
+    prenet_dropout_rate: 0.5
+    n_lstm_decoder: 1
+    reduction_factor: 1
+    decoder_lstm_units: 1024
+    attention_dim: 128
+    attention_filters: 32
+    attention_kernel: 31
+    n_mels: 80
+    n_conv_postnet: 5
+    postnet_conv_filters: 512
+    postnet_conv_kernel_sizes: 5
+    postnet_dropout_rate: 0.1
+    attention_type: "lsa"
+
+###########################################################
+#                  DATA LOADER SETTING                    #
+###########################################################
+batch_size: 32              # Batch size for each GPU with assuming that gradient_accumulation_steps == 1.
+remove_short_samples: true # Whether to remove samples the length of which are less than batch_max_steps.
+allow_cache: true           # Whether to allow cache in dataset. If true, it requires cpu memory.
+mel_length_threshold: 32    # remove all targets has mel_length <= 32 
+is_shuffle: true            # shuffle dataset after each epoch.
+use_fixed_shapes: true      # use_fixed_shapes for training (2x speed-up)
+                            # refer (https://github.com/dathudeptrai/TensorflowTTS/issues/34#issuecomment-642309118)
+
+###########################################################
+#             OPTIMIZER & SCHEDULER SETTING               #
+###########################################################
+optimizer_params:
+    initial_learning_rate: 0.001
+    end_learning_rate: 0.00001
+    decay_steps: 150000          # < train_max_steps is recommend.
+    warmup_proportion: 0.02
+    weight_decay: 0.001
+    
+gradient_accumulation_steps: 1
+var_train_expr: null  # trainable variable expr (eg. 'embeddings|decoder_cell' )
+                      # must separate by |. if var_train_expr is null then we 
+                      # training all variables.
+###########################################################
+#                    INTERVAL SETTING                     #
+###########################################################
+train_max_steps: 200000                 # Number of training steps.
+save_interval_steps: 2000               # Interval steps to save checkpoint.
+eval_interval_steps: 500                # Interval steps to evaluate the network.
+log_interval_steps: 200                 # Interval steps to record the training log.
+start_schedule_teacher_forcing: 200001  # don't need to apply schedule teacher forcing.
+start_ratio_value: 0.5                  # start ratio of scheduled teacher forcing.
+schedule_decay_steps: 50000             # decay step scheduled teacher forcing.
+end_ratio_value: 0.0                    # end ratio of scheduled teacher forcing.
+###########################################################
+#                     OTHER SETTING                       #
+###########################################################
+num_save_intermediate_results: 1  # Number of results to be saved as intermediate results.

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7761e61d0dd3bbe9387ff6191d1507d9fd308d6117c8d3ec2f8151c6f9ea4470
+size 127842184

processor.json

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