update new multi voice modelcard

README.md

@@ -1,5 +1,164 @@
 ---
+tasks:
+- text-to-speech
+domain:
+- audio
+frameworks:
+- tensorflow
+- pytorch
+backbone:
+- transformer
+metrics:
+- MOS
 license: Apache License 2.0
+tags:
+- Alibaba
+- tts
+- hifigan
+- sambert
+- text-to-speech
+- zhcn
+widgets:
+  - task: text-to-speech
+    inputs:
+      - type: text 
+        name: input 
+        title: 文本 
+        validator: 
+          max_words: 30
+    examples:
+      - name: 1
+        title: 示例1 
+        inputs:
+          - name: input
+            data: 北京今天天气怎么样
+    inferencespec:
+      cpu: 4 #CPU数量
+      memory: 8192 
+      gpu: 1 #GPU数量
+      gpu_memory: 8192 
 ---
-#### Clone with HTTP
+
-* http://www.modelscope.cn/damo/speech_sambert-hifigan_tts_zh-cn_16k.git
+# Sambert-Hifigan模型介绍
+
+
+## 框架描述
+拼接法和参数法是两种Text-To-Speech(TTS)技术路线。近年来参数TTS系统获得了广泛的应用，故此处仅涉及参数法。
+
+参数TTS系统可分为两大模块：前端和后端。
+前端包含文本正则、分词、多音字预测、文本转音素和韵律预测等模块，它的功能是把输入文本进行解析，获得音素、音调、停顿和位置等语言学特征。
+后端包含时长模型、声学模型和声码器，它的功能是将语言学特征转换为语音。其中，时长模型的功能是给定语言学特征，获得每一个建模单元（例如:音素）的时长信息；声学模型则基于语言学特征和时长信息预测声学特征；声码器则将声学特征转换为对应的语音波形。
+
+其系统结构如[图1]所示：
+
+![系统结构](description/tts-system.png)
+
+前端模块我们采用模型结合规则的方式灵活处理各种场景下的文本，后端模块则采用SAM-BERT + HIFIGAN提供高表现力的流式合成效果。
+
+### 声学模型SAM-BERT
+后端模块中声学模型采用自研的SAM-BERT,将时长模型和声学模型联合进行建模。结构如[图2]所示
+```
+1. Backbone采用Self-Attention-Mechanism(SAM)，提升模型建模能力。
+2. Encoder部分采用BERT进行初始化，引入更多文本信息，提升合成韵律。
+3. Variance Adaptor对音素级别的韵律(基频、能量、时长)轮廓进行粗粒度的预测，再通过decoder进行帧级别细粒度的建模;并在时长预测时考虑到其与基频、能量的关联信息，结合自回归结构，进一步提升韵律自然度.
+4. Decoder部分采用PNCA AR-Decoder[@li2020robutrans]，自然支持流式合成。
+```
+
+
+![SAMBERT结构](description/sambert.png)
+
+### 声码器模型:HIFI-GAN
+后端模块中声码器采用HIFI-GAN, 基于GAN的方式利用判别器(Discriminator)来指导声码器(即生成器Generator)的训练，相较于经典的自回归式逐样本点CE训练, 训练方式更加自然，在生成效率和效果上具有明显的优势。其系统结构如[图3]所示：
+
+![系统结构](description/hifigan.png)
+
+在HIFI-GAN开源工作[1]的基础上，我们针对16k, 48k采样率下的模型结构进行了调优设计，并提供了基于因果卷积的低时延流式生成和chunk流式生成机制，可与声学模型配合支持CPU、GPU等硬件条件下的实时流式合成。
+
+## 使用方式和范围
+
+使用方式：
+* 直接推理，输入为以测试标签为键以待合成文本为值的Dict对象，默认使用voices文件夹下voices.json中第一个发音人
+
+使用范围:
+* 适用于英式英文的语音合成场景，输入文本使用utf-8编码，整体长度建议不超过30字
+
+目标场景:
+* 各种语音合成任务，比如配音，虚拟主播，数字人等
+
+### 如何使用
+目前仅支持Linux使用，暂不支持Windows及Mac使用。在安装完成ModelScope-lib之后即可使用，支持的发音人名称请参考voices文件夹下面的voices.json
+
+#### 代码范例
+```Python
+from modelscope.metainfo import Pipelines
+from modelscope.models import Model
+from modelscope.pipelines import pipeline
+from modelscope.pipelines.outputs import OutputKeys
+from modelscope.utils.constant import Tasks, Fields
+
+single_test_case_label = 'test_case_label_0'
+text = '待合成文本'
+voice = 'zhitian_emo'
+model_id = 'damo\speech_sambert-hifigan_tts_zhcn_16k'
+sambert_hifigan_tts = pipeline(task=Tasks.text_to_speech, model=model_id)
+test_cases = {single_test_case_label: text}
+test_cases['voice'] = voice
+output = sambert_tts(test_cases)
+# save to file output.wav
+write('output.wav', 16000, output[OutputKeys.OUTPUT_WAV][single_test_case_label])
+```
+
+### 模型局限性以及可能的偏差
+* 该发音人支持中文及英文混合，TN规则为中文
+* 目前支持发音人zhitian_emo，zhiyan_emo，zhizhe_emo，zhibei_emo
+
+## 训练数据介绍
+暂无
+
+## 模型训练流程
+暂无
+
+### 预处理
+暂无
+
+## 数据评估及结果
+暂无
+
+## 引用
+如果你觉得这个该模型对有所帮助，请考虑引用下面的相关的论文：
+
+```BibTeX
+@inproceedings{li2020robutrans,
+  title={Robutrans: A robust transformer-based text-to-speech model},
+  author={Li, Naihan and Liu, Yanqing and Wu, Yu and Liu, Shujie and Zhao, Sheng and Liu, Ming},
+  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
+  volume={34},
+  number={05},
+  pages={8228--8235},
+  year={2020}
+}
+```
+
+```BibTeX
+@article{devlin2018bert,
+  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
+  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
+  journal={arXiv preprint arXiv:1810.04805},
+  year={2018}
+}
+```
+```BibTeX
+@article{kong2020hifi,
+  title={Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis},
+  author={Kong, Jungil and Kim, Jaehyeon and Bae, Jaekyoung},
+  journal={Advances in Neural Information Processing Systems},
+  volume={33},
+  pages={17022--17033},
+  year={2020}
+}
+```
+
+
+
+- [1] https://github.com/jik876/hifi-gan 
+

configuration.json

@@ -0,0 +1,127 @@
+{
+  "framework": "tensorflow",
+  "task" : "text-to-speech",
+  "model" : {
+    "type" : "sambert-hifigan",
+    "lang_type" : "zhcn",
+    "sample_rate" : 16000,
+    "am": {
+      "cleaners":"english_cleaners",
+
+      "num_mels":80,
+      "sample_rate":16000,
+      "frame_shift_ms":12.5,
+
+      
+      "embedding_dim":512,
+      "encoder_n_conv_layers":3,
+      "encoder_filters":256,
+      "encoder_kernel_size":5,
+
+      "encoder_num_layers":8,
+      "encoder_num_units":128,
+      "encoder_num_heads":8,
+      "encoder_ffn_inner_dim":1024,
+      "encoder_dropout":0.1,
+      "encoder_attention_dropout":0.1,
+      "encoder_relu_dropout":0.1,
+      "encoder_projection_units":32,
+
+      "predictor_filter_size":41,
+      "predictor_fsmn_num_layers":3,
+      "predictor_dnn_num_layers":0,
+      "predictor_num_memory_units":128,
+      "predictor_ffn_inner_dim":256,
+      "predictor_dropout":0.1,
+      "predictor_shift":0,
+
+      "predictor_prenet_units":[128, 128],
+      "predictor_lstm_units":128,
+
+      "prenet_units":[256, 256],
+      "prenet_proj_units":128,
+
+      "decoder_num_layers":12,
+      "decoder_num_units":128,
+      "decoder_num_heads":8,
+      "decoder_ffn_inner_dim":1024,
+      "decoder_dropout":0.1,
+      "decoder_attention_dropout":0.1,
+      "decoder_relu_dropout":0.1,
+
+      "outputs_per_step":3,
+
+      "postnet_filter_size":41,
+      "postnet_fsmn_num_layers":4,
+      "postnet_dnn_num_layers":0,
+      "postnet_num_memory_units":256,
+      "postnet_ffn_inner_dim":512,
+      "postnet_dropout":0.1,
+      "postnet_shift":17,
+      "postnet_lstm_units":128, 
+
+      
+      "dur_scale":1.0,
+
+      "batch_size":32,
+      "adam_beta1":0.9,
+      "adam_beta2":0.999,
+      "initial_learning_rate":0.002,
+      "decay_learning_rate":true,
+      "use_cmudict":false,                 
+
+      "lfeat_type_list":"sy,tone,syllable_flag,word_segment,emo_category,speaker",
+
+      "guided_attention":false,
+      "guided_attention_2g_squared":0.08,
+      "guided_attention_loss_weight":1.0,
+
+      "free_run":false,
+
+      "X_band_width":40,
+      "H_band_width":40,
+
+      "max_len":900
+    },
+    "vocoder" : {
+      "resblock": "1",
+      "num_gpus": 1,
+      "batch_size": 16,
+      "learning_rate": 0.0002,
+      "adam_b1": 0.8,
+      "adam_b2": 0.99,
+      "lr_decay": 0.999,
+      "seed": 1234,
+
+      "upsample_rates": [10,5,2,2],
+      "upsample_kernel_sizes": [20,11,4,4],
+      "upsample_initial_channel": 256,
+      "resblock_kernel_sizes": [3,7,11],
+      "resblock_dilation_sizes": [[1,3,5,7], [1,3,5,7], [1,3,5,7]],
+
+      "segment_size": 6400,
+      "num_mels": 80,
+      "num_freq": 1025,
+      "n_fft": 2048,
+      "hop_size": 200,
+      "win_size": 1000,
+
+      "sampling_rate": 16000,
+
+      "fmin": 0,
+      "fmax": 8000,
+      "fmax_for_loss": null,
+
+      "num_workers": 4,
+
+      "dist_config": {
+          "dist_backend": "nccl",
+          "dist_url": "tcp://localhost:54312",
+          "world_size": 1
+      }
+    }
+  },
+  "pipeline": {
+     "type": "sambert-hifigan-tts"
+  }
+}