Hugging Face's logo

Spaces:
lanzhiwang
/
Translate-100-languages
Runtime error

App Files Community
Translate-100-languages
File size: 5,732 Bytes 
e142967
 
 
 
3d3c0dc
e142967
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
164ce9d
e142967
16e9c22
e142967
 
 
 
 
 
d5bdfe9
 
 
 
 
 
 
 
 
 
 
e142967
 
 
 
 
b222d67
 
 
e142967
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
 
import streamlit as st
from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration
import time
import torch
import logging

if torch.cuda.is_available():
    device = torch.device("cuda:0")
else:
    device = torch.device("cpu")
    logging.warning("GPU not found, using CPU, translation will be very slow.")

st.set_page_config(page_title="M2M100 Translator")

lang_id = {
    "Afrikaans": "af",
    "Amharic": "am",
    "Arabic": "ar",
    "Asturian": "ast",
    "Azerbaijani": "az",
    "Bashkir": "ba",
    "Belarusian": "be",
    "Bulgarian": "bg",
    "Bengali": "bn",
    "Breton": "br",
    "Bosnian": "bs",
    "Catalan": "ca",
    "Cebuano": "ceb",
    "Czech": "cs",
    "Welsh": "cy",
    "Danish": "da",
    "German": "de",
    "Greeek": "el",
    "English": "en",
    "Spanish": "es",
    "Estonian": "et",
    "Persian": "fa",
    "Fulah": "ff",
    "Finnish": "fi",
    "French": "fr",
    "Western Frisian": "fy",
    "Irish": "ga",
    "Gaelic": "gd",
    "Galician": "gl",
    "Gujarati": "gu",
    "Hausa": "ha",
    "Hebrew": "he",
    "Hindi": "hi",
    "Croatian": "hr",
    "Haitian": "ht",
    "Hungarian": "hu",
    "Armenian": "hy",
    "Indonesian": "id",
    "Igbo": "ig",
    "Iloko": "ilo",
    "Icelandic": "is",
    "Italian": "it",
    "Japanese": "ja",
    "Javanese": "jv",
    "Georgian": "ka",
    "Kazakh": "kk",
    "Central Khmer": "km",
    "Kannada": "kn",
    "Korean": "ko",
    "Luxembourgish": "lb",
    "Ganda": "lg",
    "Lingala": "ln",
    "Lao": "lo",
    "Lithuanian": "lt",
    "Latvian": "lv",
    "Malagasy": "mg",
    "Macedonian": "mk",
    "Malayalam": "ml",
    "Mongolian": "mn",
    "Marathi": "mr",
    "Malay": "ms",
    "Burmese": "my",
    "Nepali": "ne",
    "Dutch": "nl",
    "Norwegian": "no",
    "Northern Sotho": "ns",
    "Occitan": "oc",
    "Oriya": "or",
    "Panjabi": "pa",
    "Polish": "pl",
    "Pushto": "ps",
    "Portuguese": "pt",
    "Romanian": "ro",
    "Russian": "ru",
    "Sindhi": "sd",
    "Sinhala": "si",
    "Slovak": "sk",
    "Slovenian": "sl",
    "Somali": "so",
    "Albanian": "sq",
    "Serbian": "sr",
    "Swati": "ss",
    "Sundanese": "su",
    "Swedish": "sv",
    "Swahili": "sw",
    "Tamil": "ta",
    "Thai": "th",
    "Tagalog": "tl",
    "Tswana": "tn",
    "Turkish": "tr",
    "Ukrainian": "uk",
    "Urdu": "ur",
    "Uzbek": "uz",
    "Vietnamese": "vi",
    "Wolof": "wo",
    "Xhosa": "xh",
    "Yiddish": "yi",
    "Yoruba": "yo",
    "Chinese": "zh",
    "Zulu": "zu",
}


@st.cache_data
def load_model(
    pretrained_model: str = "facebook/m2m100_1.2B",
    cache_dir: str = "models/",
):
    tokenizer = M2M100Tokenizer.from_pretrained(pretrained_model, cache_dir=cache_dir)
    model = M2M100ForConditionalGeneration.from_pretrained(
        pretrained_model, cache_dir=cache_dir
    ).to(device)
    """
    在PyTorch中，`model.eval()`是用来将模型设置为评估（evaluation）模式的方法。在深度学习中，训练和评估两个阶段的模型行为可能会有所不同。以下是`model.eval()`的主要作用：

    1. **Batch Normalization和Dropout的影响：**
    - 在训练阶段，`Batch Normalization`和`Dropout`等层的行为通常是不同的。在训练时，`Batch Normalization`使用批次统计信息来规范化输入，而`Dropout`层会随机丢弃一些神经元。在评估阶段，我们通常希望使用整个数据集的统计信息来规范化，而不是每个批次的统计信息，并且不再需要随机丢弃神经元。因此，通过执行`model.eval()`，模型会切换到评估模式，从而确保这些层的行为在评估时是正确的。

    2. **梯度计算的关闭：**
    - 在评估模式下，PyTorch会关闭自动求导（autograd）的计算图，这样可以避免不必要的梯度计算和内存消耗。在训练时，我们通常需要计算梯度以进行反向传播和参数更新，而在评估时，我们只对模型的前向传播感兴趣，因此关闭梯度计算可以提高评估的速度和减少内存使用。

    总的来说，执行`model.eval()`是为了确保在评估阶段模型的行为和性能是正确的，并且可以提高评估时的效率。
    """
    model.eval()
    return tokenizer, model


st.title("M2M100 Translator")
st.write("M2M100 is a multilingual encoder-decoder (seq-to-seq) model trained for Many-to-Many multilingual translation. It was introduced in this paper https://arxiv.org/abs/2010.11125 and first released in https://github.com/pytorch/fairseq/tree/master/examples/m2m_100 repository. The model that can directly translate between the 9,900 directions of 100 languages.\n")

st.write(" This demo uses the facebook/m2m100_1.2B model. For local inference see https://github.com/ikergarcia1996/Easy-Translate")


user_input: str = st.text_area(
    "Input text",
    height=200,
    max_chars=5120,
)

source_lang = st.selectbox(label="Source language", options=list(lang_id.keys()))
target_lang = st.selectbox(label="Target language", options=list(lang_id.keys()))

if st.button("Run"):
    time_start = time.time()
    tokenizer, model = load_model()

    src_lang = lang_id[source_lang]
    trg_lang = lang_id[target_lang]
    tokenizer.src_lang = src_lang
    with torch.no_grad():
        encoded_input = tokenizer(user_input, return_tensors="pt").to(device)
        generated_tokens = model.generate(
            **encoded_input, forced_bos_token_id=tokenizer.get_lang_id(trg_lang)
        )
        translated_text = tokenizer.batch_decode(
            generated_tokens, skip_special_tokens=True
        )[0]

    time_end = time.time()
    st.success(translated_text)

    st.write(f"Computation time: {round((time_end-time_start),3)} segs")