## RyanSpeech model (based on ESPnet2)

### `espnet/english_male_ryanspeech_tacotron`
This model was trained by [Rohola Zandie](https://scholar.google.com/citations?user=xv0jIe0AAAAJ&hl=en) using ryanspeech recipe in [espnet](https://github.com/espnet/espnet/). For the best results you need to download the vocoder separately from [here](https://drive.google.com/file/d/10GYvB_mIKzXzSjD67tSnBhknZRoBjsNb/view?usp=sharing) and then use the following code:

```

from espnet2.bin.tts_inference import Text2Speech
from scipy.io.wavfile import write

model = Text2Speech.from_pretrained(
    model_file="espnet/english_male_ryanspeech_tacotron",
    vocoder_file="path_to_vocoder/train_nodev_parallel_wavegan.v1.long/checkpoint-1000000steps.pkl"
)

output = model("This is a simple test.")

write("x.wav", 22050, output['wav'].numpy())
```


## Download the dataset
You can download RyanSpeech dataset from [here](https://www.kaggle.com/datasets/roholazandie/ryanspeech) or here.

## TTS config

<details><summary>expand</summary>

```
config: conf/train.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/tts_train_raw_phn_tacotron_g2p_en_no_space
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 200
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
-   - valid
    - loss
    - min
-   - train
    - loss
    - min
keep_nbest_models: 5
grad_clip: 1.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
pretrain_path: []
pretrain_key: []
num_iters_per_epoch: 500
batch_size: 20
valid_batch_size: null
batch_bins: 5120000
valid_batch_bins: null
train_shape_file:
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/text_shape.phn
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/speech_shape
valid_shape_file:
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/text_shape.phn
- exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/valid/speech_shape
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 204800
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
-   - dump/raw/tr_no_dev/text
    - text
    - text
-   - dump/raw/tr_no_dev/wav.scp
    - speech
    - sound
valid_data_path_and_name_and_type:
-   - dump/raw/dev/text
    - text
    - text
-   - dump/raw/dev/wav.scp
    - speech
    - sound
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
    lr: 0.001
    eps: 1.0e-06
    weight_decay: 0.0
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- AH0
- T
- N
- S
- R
- D
- L
- K
- IH1
- M
- EH1
- Z
- DH
- UW1
- AE1
- IH0
- AY1
- AH1
- W
- .
- P
- F
- IY1
- V
- ER0
- AA1
- B
- AO1
- HH
- EY1
- IY0
- ','
- Y
- NG
- OW1
- G
- AW1
- TH
- SH
- UH1
- '?'
- ER1
- JH
- CH
- OW0
- OW2
- EH2
- IH2
- EY2
- AA2
- AE2
- AY2
- ''''
- OY1
- UW0
- '!'
- AO2
- EH0
- ZH
- AH2
- AE0
- UW2
- AA0
- AY0
- IY2
- AW2
- AO0
- EY0
- ER2
- UH2
- '...'
- AW0
- UH0
- OY2
- <sos/eos>
odim: null
model_conf: {}
use_preprocessor: true
token_type: phn
bpemodel: null
non_linguistic_symbols: null
cleaner: tacotron
g2p: g2p_en_no_space
feats_extract: fbank
feats_extract_conf:
    fs: 22050
    fmin: 80
    fmax: 7600
    n_mels: 80
    hop_length: 256
    n_fft: 1024
    win_length: null
normalize: global_mvn
normalize_conf:
    stats_file: exp/tts_stats_raw_phn_tacotron_g2p_en_no_space/train/feats_stats.npz
tts: tacotron2
tts_conf:
    embed_dim: 512
    elayers: 1
    eunits: 512
    econv_layers: 3
    econv_chans: 512
    econv_filts: 5
    atype: location
    adim: 512
    aconv_chans: 32
    aconv_filts: 15
    cumulate_att_w: true
    dlayers: 2
    dunits: 1024
    prenet_layers: 2
    prenet_units: 256
    postnet_layers: 5
    postnet_chans: 512
    postnet_filts: 5
    output_activation: null
    use_batch_norm: true
    use_concate: true
    use_residual: false
    dropout_rate: 0.5
    zoneout_rate: 0.1
    reduction_factor: 1
    spk_embed_dim: null
    use_masking: true
    bce_pos_weight: 5.0
    use_guided_attn_loss: true
    guided_attn_loss_sigma: 0.4
    guided_attn_loss_lambda: 1.0
pitch_extract: null
pitch_extract_conf: {}
pitch_normalize: null
pitch_normalize_conf: {}
energy_extract: null
energy_extract_conf: {}
energy_normalize: null
energy_normalize_conf: {}
required:
- output_dir
- token_list
distributed: false

```

</details>


### Citing RyanSpeech

```BibTex
@inproceedings{Zandie2021RyanSpeechAC,
  title={RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis},
  author={Rohola Zandie and Mohammad H. Mahoor and Julia Madsen and Eshrat S. Emamian},
  booktitle={Interspeech},
  year={2021}
}
```
{"language": "en", "license": "cc-by-nc-4.0", "tags": ["espnet", "audio", "text-to-speech"], "datasets": ["ryanspeech"], "widget": [{"text": "This seems a very pleasant place, and I think I shall enjoy myself very much."}]}
espnet audio text-to-speech en dataset:ryanspeech license:cc-by-nc-4.0 has_space region:us
TAGS
#espnet #audio #text-to-speech #en #dataset-ryanspeech #license-cc-by-nc-4.0 #has_space #region-us 

## RyanSpeech model (based on ESPnet2)

### 'espnet/english_male_ryanspeech_tacotron'
This model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code:




## Download the dataset
You can download RyanSpeech dataset from here or here.

## TTS config

<details><summary>expand</summary>



</details>


### Citing RyanSpeech


## RyanSpeech model (based on ESPnet2) ### 'espnet/english_male_ryanspeech_tacotron'
This model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code: ## Download the dataset
You can download RyanSpeech dataset from here or here. ## TTS config

<details><summary>expand</summary>



</details> ### Citing RyanSpeech
TAGS
#espnet #audio #text-to-speech #en #dataset-ryanspeech #license-cc-by-nc-4.0 #has_space #region-us 
 ## RyanSpeech model (based on ESPnet2) ### 'espnet/english_male_ryanspeech_tacotron'
This model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code: ## Download the dataset
You can download RyanSpeech dataset from here or here. ## TTS config

<details><summary>expand</summary>



</details> ### Citing RyanSpeech
passage: TAGS
#espnet #audio #text-to-speech #en #dataset-ryanspeech #license-cc-by-nc-4.0 #has_space #region-us 
## RyanSpeech model (based on ESPnet2)### 'espnet/english_male_ryanspeech_tacotron'
This model was trained by Rohola Zandie using ryanspeech recipe in espnet. For the best results you need to download the vocoder separately from here and then use the following code:## Download the dataset
You can download RyanSpeech dataset from here or here.## TTS config

<details><summary>expand</summary>



</details>### Citing RyanSpeech