Update app.py

app.py

@@ -1,694 +1,694 @@
-# Copyright (c) Meta Platforms, Inc. and affiliates
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-
-from __future__ import annotations
-
-import gradio as gr
-import numpy as np
-# import torch
-
-
-from gradio_client import Client
-
-client = Client("https://facebook-seamless-m4t.hf.space/")
-
-DESCRIPTION = """
-
-# SM4T
-
-Ứng dụng có thể chuyển đổi giọng nói hoặc chữ viết sang giọng nói hoặc chữ viết của một ngôn ngữ khác.
-\nHiện tại SM4T đã hỗ trợ 94 ngôn ngữ khác nhau.
-
-"""
-
-TASK_NAMES = [
-    "S2ST (Speech to Speech translation)",
-    "S2TT (Speech to Text translation)",
-    "T2ST (Text to Speech translation)",
-    "T2TT (Text to Text translation)",
-    "ASR (Automatic Speech Recognition)",
-]
-
-# Language dict
-language_code_to_name = {
-    "afr": "Afrikaans",
-    "amh": "Amharic",
-    "arb": "Modern Standard Arabic",
-    "ary": "Moroccan Arabic",
-    "arz": "Egyptian Arabic",
-    "asm": "Assamese",
-    "ast": "Asturian",
-    "azj": "North Azerbaijani",
-    "bel": "Belarusian",
-    "ben": "Bengali",
-    "bos": "Bosnian",
-    "bul": "Bulgarian",
-    "cat": "Catalan",
-    "ceb": "Cebuano",
-    "ces": "Czech",
-    "ckb": "Central Kurdish",
-    "cmn": "Mandarin Chinese",
-    "cym": "Welsh",
-    "dan": "Danish",
-    "deu": "German",
-    "ell": "Greek",
-    "eng": "English",
-    "est": "Estonian",
-    "eus": "Basque",
-    "fin": "Finnish",
-    "fra": "French",
-    "gaz": "West Central Oromo",
-    "gle": "Irish",
-    "glg": "Galician",
-    "guj": "Gujarati",
-    "heb": "Hebrew",
-    "hin": "Hindi",
-    "hrv": "Croatian",
-    "hun": "Hungarian",
-    "hye": "Armenian",
-    "ibo": "Igbo",
-    "ind": "Indonesian",
-    "isl": "Icelandic",
-    "ita": "Italian",
-    "jav": "Javanese",
-    "jpn": "Japanese",
-    "kam": "Kamba",
-    "kan": "Kannada",
-    "kat": "Georgian",
-    "kaz": "Kazakh",
-    "kea": "Kabuverdianu",
-    "khk": "Halh Mongolian",
-    "khm": "Khmer",
-    "kir": "Kyrgyz",
-    "kor": "Korean",
-    "lao": "Lao",
-    "lit": "Lithuanian",
-    "ltz": "Luxembourgish",
-    "lug": "Ganda",
-    "luo": "Luo",
-    "lvs": "Standard Latvian",
-    "mai": "Maithili",
-    "mal": "Malayalam",
-    "mar": "Marathi",
-    "mkd": "Macedonian",
-    "mlt": "Maltese",
-    "mni": "Meitei",
-    "mya": "Burmese",
-    "nld": "Dutch",
-    "nno": "Norwegian Nynorsk",
-    "nob": "Norwegian Bokm\u00e5l",
-    "npi": "Nepali",
-    "nya": "Nyanja",
-    "oci": "Occitan",
-    "ory": "Odia",
-    "pan": "Punjabi",
-    "pbt": "Southern Pashto",
-    "pes": "Western Persian",
-    "pol": "Polish",
-    "por": "Portuguese",
-    "ron": "Romanian",
-    "rus": "Russian",
-    "slk": "Slovak",
-    "slv": "Slovenian",
-    "sna": "Shona",
-    "snd": "Sindhi",
-    "som": "Somali",
-    "spa": "Spanish",
-    "srp": "Serbian",
-    "swe": "Swedish",
-    "swh": "Swahili",
-    "tam": "Tamil",
-    "tel": "Telugu",
-    "tgk": "Tajik",
-    "tgl": "Tagalog",
-    "tha": "Thai",
-    "tur": "Turkish",
-    "ukr": "Ukrainian",
-    "urd": "Urdu",
-    "uzn": "Northern Uzbek",
-    "vie": "Vietnamese",
-    "xho": "Xhosa",
-    "yor": "Yoruba",
-    "yue": "Cantonese",
-    "zlm": "Colloquial Malay",
-    "zsm": "Standard Malay",
-    "zul": "Zulu",
-}
-LANGUAGE_NAME_TO_CODE = {v: k for k, v in language_code_to_name.items()}
-
-# Source langs: S2ST / S2TT / ASR don't need source lang
-# T2TT / T2ST use this
-text_source_language_codes = [
-    "afr",
-    "amh",
-    "arb",
-    "ary",
-    "arz",
-    "asm",
-    "azj",
-    "bel",
-    "ben",
-    "bos",
-    "bul",
-    "cat",
-    "ceb",
-    "ces",
-    "ckb",
-    "cmn",
-    "cym",
-    "dan",
-    "deu",
-    "ell",
-    "eng",
-    "est",
-    "eus",
-    "fin",
-    "fra",
-    "gaz",
-    "gle",
-    "glg",
-    "guj",
-    "heb",
-    "hin",
-    "hrv",
-    "hun",
-    "hye",
-    "ibo",
-    "ind",
-    "isl",
-    "ita",
-    "jav",
-    "jpn",
-    "kan",
-    "kat",
-    "kaz",
-    "khk",
-    "khm",
-    "kir",
-    "kor",
-    "lao",
-    "lit",
-    "lug",
-    "luo",
-    "lvs",
-    "mai",
-    "mal",
-    "mar",
-    "mkd",
-    "mlt",
-    "mni",
-    "mya",
-    "nld",
-    "nno",
-    "nob",
-    "npi",
-    "nya",
-    "ory",
-    "pan",
-    "pbt",
-    "pes",
-    "pol",
-    "por",
-    "ron",
-    "rus",
-    "slk",
-    "slv",
-    "sna",
-    "snd",
-    "som",
-    "spa",
-    "srp",
-    "swe",
-    "swh",
-    "tam",
-    "tel",
-    "tgk",
-    "tgl",
-    "tha",
-    "tur",
-    "ukr",
-    "urd",
-    "uzn",
-    "vie",
-    "yor",
-    "yue",
-    "zsm",
-    "zul",
-]
-TEXT_SOURCE_LANGUAGE_NAMES = sorted(
-    [language_code_to_name[code] for code in text_source_language_codes]
-)
-
-# Target langs:
-# S2ST / T2ST
-s2st_target_language_codes = [
-    "eng",
-    "arb",
-    "ben",
-    "cat",
-    "ces",
-    "cmn",
-    "cym",
-    "dan",
-    "deu",
-    "est",
-    "fin",
-    "fra",
-    "hin",
-    "ind",
-    "ita",
-    "jpn",
-    "kor",
-    "mlt",
-    "nld",
-    "pes",
-    "pol",
-    "por",
-    "ron",
-    "rus",
-    "slk",
-    "spa",
-    "swe",
-    "swh",
-    "tel",
-    "tgl",
-    "tha",
-    "tur",
-    "ukr",
-    "urd",
-    "uzn",
-    "vie",
-]
-S2ST_TARGET_LANGUAGE_NAMES = sorted(
-    [language_code_to_name[code] for code in s2st_target_language_codes]
-)
-# S2TT / ASR
-S2TT_TARGET_LANGUAGE_NAMES = TEXT_SOURCE_LANGUAGE_NAMES
-# T2TT
-T2TT_TARGET_LANGUAGE_NAMES = TEXT_SOURCE_LANGUAGE_NAMES
-
-# Download sample input audio files
-filenames = ["assets/sample_input.mp3", "assets/sample_input_2.mp3"]
-# for filename in filenames:
-#     hf_hub_download(
-#         repo_id="facebook/seamless_m4t",
-#         repo_type="space",
-#         filename=filename,
-#         local_dir=".",
-#     )
-
-AUDIO_SAMPLE_RATE = 16000.0
-MAX_INPUT_AUDIO_LENGTH = 60  # in seconds
-DEFAULT_TARGET_LANGUAGE = "French"
-
-# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-
-def api_predict(
-    task_name: str,
-    audio_source: str,
-    input_audio_mic: str | None,
-    input_audio_file: str | None,
-    input_text: str | None,
-    source_language: str | None,
-    target_language: str,):
-
-    audio_out, text_out = client.predict(task_name, 
-                                        audio_source, 
-                                        input_audio_mic, 
-                                        input_audio_file, 
-                                        input_text, 
-                                        source_language, 
-                                        target_language, 
-                                        api_name="/run")
-    return audio_out, text_out
-
-
-
-
-
-def process_s2st_example(
-    input_audio_file: str, target_language: str
-) -> tuple[tuple[int, np.ndarray] | None, str]:
-    return api_predict(
-        task_name="S2ST",
-        audio_source="file",
-        input_audio_mic=None,
-        input_audio_file=input_audio_file,
-        input_text=None,
-        source_language=None,
-        target_language=target_language,
-    )
-
-
-def process_s2tt_example(
-    input_audio_file: str, target_language: str
-) -> tuple[tuple[int, np.ndarray] | None, str]:
-    return api_predict(
-        task_name="S2TT",
-        audio_source="file",
-        input_audio_mic=None,
-        input_audio_file=input_audio_file,
-        input_text=None,
-        source_language=None,
-        target_language=target_language,
-    )
-
-
-def process_t2st_example(
-    input_text: str, source_language: str, target_language: str
-) -> tuple[tuple[int, np.ndarray] | None, str]:
-    return api_predict(
-        task_name="T2ST",
-        audio_source="",
-        input_audio_mic=None,
-        input_audio_file=None,
-        input_text=input_text,
-        source_language=source_language,
-        target_language=target_language,
-    )
-
-
-def process_t2tt_example(
-    input_text: str, source_language: str, target_language: str
-) -> tuple[tuple[int, np.ndarray] | None, str]:
-    return api_predict(
-        task_name="T2TT",
-        audio_source="",
-        input_audio_mic=None,
-        input_audio_file=None,
-        input_text=input_text,
-        source_language=source_language,
-        target_language=target_language,
-    )
-
-
-def process_asr_example(
-    input_audio_file: str, target_language: str
-) -> tuple[tuple[int, np.ndarray] | None, str]:
-    return api_predict(
-        task_name="ASR",
-        audio_source="file",
-        input_audio_mic=None,
-        input_audio_file=input_audio_file,
-        input_text=None,
-        source_language=None,
-        target_language=target_language,
-    )
-
-
-def update_audio_ui(audio_source: str) -> tuple[dict, dict]:
-    mic = audio_source == "microphone"
-    return (
-        gr.update(visible=mic, value=None),  # input_audio_mic
-        gr.update(visible=not mic, value=None),  # input_audio_file
-    )
-
-
-def update_input_ui(task_name: str) -> tuple[dict, dict, dict, dict]:
-    task_name = task_name.split()[0]
-    if task_name == "S2ST":
-        return (
-            gr.update(visible=True),  # audio_box
-            gr.update(visible=False),  # input_text
-            gr.update(visible=False),  # source_language
-            gr.update(
-                visible=True,
-                choices=S2ST_TARGET_LANGUAGE_NAMES,
-                value=DEFAULT_TARGET_LANGUAGE,
-            ),  # target_language
-        )
-    elif task_name == "S2TT":
-        return (
-            gr.update(visible=True),  # audio_box
-            gr.update(visible=False),  # input_text
-            gr.update(visible=False),  # source_language
-            gr.update(
-                visible=True,
-                choices=S2TT_TARGET_LANGUAGE_NAMES,
-                value=DEFAULT_TARGET_LANGUAGE,
-            ),  # target_language
-        )
-    elif task_name == "T2ST":
-        return (
-            gr.update(visible=False),  # audio_box
-            gr.update(visible=True),  # input_text
-            gr.update(visible=True),  # source_language
-            gr.update(
-                visible=True,
-                choices=S2ST_TARGET_LANGUAGE_NAMES,
-                value=DEFAULT_TARGET_LANGUAGE,
-            ),  # target_language
-        )
-    elif task_name == "T2TT":
-        return (
-            gr.update(visible=False),  # audio_box
-            gr.update(visible=True),  # input_text
-            gr.update(visible=True),  # source_language
-            gr.update(
-                visible=True,
-                choices=T2TT_TARGET_LANGUAGE_NAMES,
-                value=DEFAULT_TARGET_LANGUAGE,
-            ),  # target_language
-        )
-    elif task_name == "ASR":
-        return (
-            gr.update(visible=True),  # audio_box
-            gr.update(visible=False),  # input_text
-            gr.update(visible=False),  # source_language
-            gr.update(
-                visible=True,
-                choices=S2TT_TARGET_LANGUAGE_NAMES,
-                value=DEFAULT_TARGET_LANGUAGE,
-            ),  # target_language
-        )
-    else:
-        raise ValueError(f"Unknown task: {task_name}")
-
-
-def update_output_ui(task_name: str) -> tuple[dict, dict]:
-    task_name = task_name.split()[0]
-    if task_name in ["S2ST", "T2ST"]:
-        return (
-            gr.update(visible=True, value=None),  # output_audio
-            gr.update(value=None),  # output_text
-        )
-    elif task_name in ["S2TT", "T2TT", "ASR"]:
-        return (
-            gr.update(visible=False, value=None),  # output_audio
-            gr.update(value=None),  # output_text
-        )
-    else:
-        raise ValueError(f"Unknown task: {task_name}")
-
-
-def update_example_ui(task_name: str) -> tuple[dict, dict, dict, dict, dict]:
-    task_name = task_name.split()[0]
-    return (
-        gr.update(visible=task_name == "S2ST"),  # s2st_example_row
-        gr.update(visible=task_name == "S2TT"),  # s2tt_example_row
-        gr.update(visible=task_name == "T2ST"),  # t2st_example_row
-        gr.update(visible=task_name == "T2TT"),  # t2tt_example_row
-        gr.update(visible=task_name == "ASR"),  # asr_example_row
-    )
-
-
-css = """
-h1 {
-  text-align: center;
-}
-
-.contain {
-  max-width: 730px;
-  margin: auto;
-  padding-top: 1.5rem;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    gr.Markdown(DESCRIPTION)
-    with gr.Group():
-        task_name = gr.Dropdown(
-            label="Task",
-            choices=TASK_NAMES,
-            value=TASK_NAMES[0],
-        )
-        with gr.Row():
-            source_language = gr.Dropdown(
-                label="Source language",
-                choices=TEXT_SOURCE_LANGUAGE_NAMES,
-                value="English",
-                visible=False,
-            )
-            target_language = gr.Dropdown(
-                label="Target language",
-                choices=S2ST_TARGET_LANGUAGE_NAMES,
-                value=DEFAULT_TARGET_LANGUAGE,
-            )
-        with gr.Row() as audio_box:
-            audio_source = gr.Radio(
-                label="Audio source",
-                choices=["file", "microphone"],
-                value="file",
-            )
-            input_audio_mic = gr.Audio(
-                label="Input speech",
-                type="filepath",
-                source="microphone",
-                visible=False,
-            )
-            input_audio_file = gr.Audio(
-                label="Input speech",
-                type="filepath",
-                source="upload",
-                visible=True,
-            )
-        input_text = gr.Textbox(label="Input text", visible=False)
-        with gr.Row():
-            btn = gr.Button("Translate")
-            btn_clean = gr.ClearButton([input_audio_mic, input_audio_file])
-        # gr.Markdown("## Text Examples")
-        with gr.Column():
-            output_audio = gr.Audio(
-                label="Translated speech",
-                autoplay=False,
-                streaming=False,
-                type="numpy",
-            )
-            output_text = gr.Textbox(label="Translated text")
-
-    with gr.Row(visible=True) as s2st_example_row:
-        s2st_examples = gr.Examples(
-            examples=[
-                ["assets/sample_input.mp3", "French"],
-                ["assets/sample_input.mp3", "Mandarin Chinese"],
-                ["assets/sample_input_2.mp3", "Hindi"],
-                ["assets/sample_input_2.mp3", "Spanish"],
-            ],
-            inputs=[input_audio_file, target_language],
-            outputs=[output_audio, output_text],
-            fn=process_s2st_example,
-        )
-    with gr.Row(visible=False) as s2tt_example_row:
-        s2tt_examples = gr.Examples(
-            examples=[
-                ["assets/sample_input.mp3", "French"],
-                ["assets/sample_input.mp3", "Mandarin Chinese"],
-                ["assets/sample_input_2.mp3", "Hindi"],
-                ["assets/sample_input_2.mp3", "Spanish"],
-            ],
-            inputs=[input_audio_file, target_language],
-            outputs=[output_audio, output_text],
-            fn=process_s2tt_example,
-        )
-    with gr.Row(visible=False) as t2st_example_row:
-        t2st_examples = gr.Examples(
-            examples=[
-                ["My favorite animal is the elephant.", "English", "French"],
-                ["My favorite animal is the elephant.", "English", "Mandarin Chinese"],
-                [
-                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
-                    "English",
-                    "Hindi",
-                ],
-                [
-                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
-                    "English",
-                    "Spanish",
-                ],
-            ],
-            inputs=[input_text, source_language, target_language],
-            outputs=[output_audio, output_text],
-            fn=process_t2st_example,
-        )
-    with gr.Row(visible=False) as t2tt_example_row:
-        t2tt_examples = gr.Examples(
-            examples=[
-                ["My favorite animal is the elephant.", "English", "French"],
-                ["My favorite animal is the elephant.", "English", "Mandarin Chinese"],
-                [
-                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
-                    "English",
-                    "Hindi",
-                ],
-                [
-                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
-                    "English",
-                    "Spanish",
-                ],
-            ],
-            inputs=[input_text, source_language, target_language],
-            outputs=[output_audio, output_text],
-            fn=process_t2tt_example,
-        )
-    with gr.Row(visible=False) as asr_example_row:
-        asr_examples = gr.Examples(
-            examples=[
-                ["assets/sample_input.mp3", "English"],
-                ["assets/sample_input_2.mp3", "English"],
-            ],
-            inputs=[input_audio_file, target_language],
-            outputs=[output_audio, output_text],
-            fn=process_asr_example,
-        )
-
-    audio_source.change(
-        fn=update_audio_ui,
-        inputs=audio_source,
-        outputs=[
-            input_audio_mic,
-            input_audio_file,
-        ],
-        queue=False,
-        api_name=False,
-    )
-    task_name.change(
-        fn=update_input_ui,
-        inputs=task_name,
-        outputs=[
-            audio_box,
-            input_text,
-            source_language,
-            target_language,
-        ],
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=update_output_ui,
-        inputs=task_name,
-        outputs=[output_audio, output_text],
-        queue=False,
-        api_name=False,
-    ).then(
-        fn=update_example_ui,
-        inputs=task_name,
-        outputs=[
-            s2st_example_row,
-            s2tt_example_row,
-            t2st_example_row,
-            t2tt_example_row,
-            asr_example_row,
-        ],
-        queue=False,
-        api_name=False,
-    )
-
-    btn.click(
-        fn=api_predict,
-        inputs=[
-            task_name,
-            audio_source,
-            input_audio_mic,
-            input_audio_file,
-            input_text,
-            source_language,
-            target_language,
-        ],
-        outputs=[output_audio, output_text],
-        api_name="run",
-    )
-
-if __name__ == "__main__":
-    demo.queue().launch()
+# Copyright (c) Meta Platforms, Inc. and affiliates
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+from __future__ import annotations
+
+import gradio as gr
+import numpy as np
+# import torch
+
+
+from gradio_client import Client
+
+client = Client("https://facebook-seamless-m4t.hf.space/")
+
+DESCRIPTION = """
+
+# SM4T
+
+Ứng dụng có thể chuyển đổi giọng nói hoặc chữ viết sang giọng nói hoặc chữ viết của một ngôn ngữ khác.
+\nHiện tại SM4T đã hỗ trợ 94 ngôn ngữ khác nhau.
+
+"""
+
+TASK_NAMES = [
+    "S2ST (Speech to Speech translation)",
+    "S2TT (Speech to Text translation)",
+    "T2ST (Text to Speech translation)",
+    "T2TT (Text to Text translation)",
+    "ASR (Automatic Speech Recognition)",
+]
+
+# Language dict
+language_code_to_name = {
+    "afr": "Afrikaans",
+    "amh": "Amharic",
+    "arb": "Modern Standard Arabic",
+    "ary": "Moroccan Arabic",
+    "arz": "Egyptian Arabic",
+    "asm": "Assamese",
+    "ast": "Asturian",
+    "azj": "North Azerbaijani",
+    "bel": "Belarusian",
+    "ben": "Bengali",
+    "bos": "Bosnian",
+    "bul": "Bulgarian",
+    "cat": "Catalan",
+    "ceb": "Cebuano",
+    "ces": "Czech",
+    "ckb": "Central Kurdish",
+    "cmn": "Mandarin Chinese",
+    "cym": "Welsh",
+    "dan": "Danish",
+    "deu": "German",
+    "ell": "Greek",
+    "eng": "English",
+    "est": "Estonian",
+    "eus": "Basque",
+    "fin": "Finnish",
+    "fra": "French",
+    "gaz": "West Central Oromo",
+    "gle": "Irish",
+    "glg": "Galician",
+    "guj": "Gujarati",
+    "heb": "Hebrew",
+    "hin": "Hindi",
+    "hrv": "Croatian",
+    "hun": "Hungarian",
+    "hye": "Armenian",
+    "ibo": "Igbo",
+    "ind": "Indonesian",
+    "isl": "Icelandic",
+    "ita": "Italian",
+    "jav": "Javanese",
+    "jpn": "Japanese",
+    "kam": "Kamba",
+    "kan": "Kannada",
+    "kat": "Georgian",
+    "kaz": "Kazakh",
+    "kea": "Kabuverdianu",
+    "khk": "Halh Mongolian",
+    "khm": "Khmer",
+    "kir": "Kyrgyz",
+    "kor": "Korean",
+    "lao": "Lao",
+    "lit": "Lithuanian",
+    "ltz": "Luxembourgish",
+    "lug": "Ganda",
+    "luo": "Luo",
+    "lvs": "Standard Latvian",
+    "mai": "Maithili",
+    "mal": "Malayalam",
+    "mar": "Marathi",
+    "mkd": "Macedonian",
+    "mlt": "Maltese",
+    "mni": "Meitei",
+    "mya": "Burmese",
+    "nld": "Dutch",
+    "nno": "Norwegian Nynorsk",
+    "nob": "Norwegian Bokm\u00e5l",
+    "npi": "Nepali",
+    "nya": "Nyanja",
+    "oci": "Occitan",
+    "ory": "Odia",
+    "pan": "Punjabi",
+    "pbt": "Southern Pashto",
+    "pes": "Western Persian",
+    "pol": "Polish",
+    "por": "Portuguese",
+    "ron": "Romanian",
+    "rus": "Russian",
+    "slk": "Slovak",
+    "slv": "Slovenian",
+    "sna": "Shona",
+    "snd": "Sindhi",
+    "som": "Somali",
+    "spa": "Spanish",
+    "srp": "Serbian",
+    "swe": "Swedish",
+    "swh": "Swahili",
+    "tam": "Tamil",
+    "tel": "Telugu",
+    "tgk": "Tajik",
+    "tgl": "Tagalog",
+    "tha": "Thai",
+    "tur": "Turkish",
+    "ukr": "Ukrainian",
+    "urd": "Urdu",
+    "uzn": "Northern Uzbek",
+    "vie": "Vietnamese",
+    "xho": "Xhosa",
+    "yor": "Yoruba",
+    "yue": "Cantonese",
+    "zlm": "Colloquial Malay",
+    "zsm": "Standard Malay",
+    "zul": "Zulu",
+}
+LANGUAGE_NAME_TO_CODE = {v: k for k, v in language_code_to_name.items()}
+
+# Source langs: S2ST / S2TT / ASR don't need source lang
+# T2TT / T2ST use this
+text_source_language_codes = [
+    "afr",
+    "amh",
+    "arb",
+    "ary",
+    "arz",
+    "asm",
+    "azj",
+    "bel",
+    "ben",
+    "bos",
+    "bul",
+    "cat",
+    "ceb",
+    "ces",
+    "ckb",
+    "cmn",
+    "cym",
+    "dan",
+    "deu",
+    "ell",
+    "eng",
+    "est",
+    "eus",
+    "fin",
+    "fra",
+    "gaz",
+    "gle",
+    "glg",
+    "guj",
+    "heb",
+    "hin",
+    "hrv",
+    "hun",
+    "hye",
+    "ibo",
+    "ind",
+    "isl",
+    "ita",
+    "jav",
+    "jpn",
+    "kan",
+    "kat",
+    "kaz",
+    "khk",
+    "khm",
+    "kir",
+    "kor",
+    "lao",
+    "lit",
+    "lug",
+    "luo",
+    "lvs",
+    "mai",
+    "mal",
+    "mar",
+    "mkd",
+    "mlt",
+    "mni",
+    "mya",
+    "nld",
+    "nno",
+    "nob",
+    "npi",
+    "nya",
+    "ory",
+    "pan",
+    "pbt",
+    "pes",
+    "pol",
+    "por",
+    "ron",
+    "rus",
+    "slk",
+    "slv",
+    "sna",
+    "snd",
+    "som",
+    "spa",
+    "srp",
+    "swe",
+    "swh",
+    "tam",
+    "tel",
+    "tgk",
+    "tgl",
+    "tha",
+    "tur",
+    "ukr",
+    "urd",
+    "uzn",
+    "vie",
+    "yor",
+    "yue",
+    "zsm",
+    "zul",
+]
+TEXT_SOURCE_LANGUAGE_NAMES = sorted(
+    [language_code_to_name[code] for code in text_source_language_codes]
+)
+
+# Target langs:
+# S2ST / T2ST
+s2st_target_language_codes = [
+    "eng",
+    "arb",
+    "ben",
+    "cat",
+    "ces",
+    "cmn",
+    "cym",
+    "dan",
+    "deu",
+    "est",
+    "fin",
+    "fra",
+    "hin",
+    "ind",
+    "ita",
+    "jpn",
+    "kor",
+    "mlt",
+    "nld",
+    "pes",
+    "pol",
+    "por",
+    "ron",
+    "rus",
+    "slk",
+    "spa",
+    "swe",
+    "swh",
+    "tel",
+    "tgl",
+    "tha",
+    "tur",
+    "ukr",
+    "urd",
+    "uzn",
+    "vie",
+]
+S2ST_TARGET_LANGUAGE_NAMES = sorted(
+    [language_code_to_name[code] for code in s2st_target_language_codes]
+)
+# S2TT / ASR
+S2TT_TARGET_LANGUAGE_NAMES = TEXT_SOURCE_LANGUAGE_NAMES
+# T2TT
+T2TT_TARGET_LANGUAGE_NAMES = TEXT_SOURCE_LANGUAGE_NAMES
+
+# Download sample input audio files
+filenames = ["assets/sample_input.mp3", "assets/sample_input_2.mp3"]
+# for filename in filenames:
+#     hf_hub_download(
+#         repo_id="facebook/seamless_m4t",
+#         repo_type="space",
+#         filename=filename,
+#         local_dir=".",
+#     )
+
+AUDIO_SAMPLE_RATE = 16000.0
+MAX_INPUT_AUDIO_LENGTH = 60  # in seconds
+DEFAULT_TARGET_LANGUAGE = "French"
+
+# device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+def api_predict(
+    task_name: str,
+    audio_source: str,
+    input_audio_mic: str | None,
+    input_audio_file: str | None,
+    input_text: str | None,
+    source_language: str | None,
+    target_language: str,):
+
+    audio_out, text_out = client.predict(task_name, 
+                                        audio_source, 
+                                        input_audio_mic, 
+                                        input_audio_file, 
+                                        input_text, 
+                                        source_language, 
+                                        target_language, 
+                                        api_name="/run")
+    return audio_out, text_out
+
+
+
+
+
+def process_s2st_example(
+    input_audio_file: str, target_language: str
+) -> tuple[tuple[int, np.ndarray] | None, str]:
+    return api_predict(
+        task_name="S2ST",
+        audio_source="file",
+        input_audio_mic=None,
+        input_audio_file=input_audio_file,
+        input_text=None,
+        source_language=None,
+        target_language=target_language,
+    )
+
+
+def process_s2tt_example(
+    input_audio_file: str, target_language: str
+) -> tuple[tuple[int, np.ndarray] | None, str]:
+    return api_predict(
+        task_name="S2TT",
+        audio_source="file",
+        input_audio_mic=None,
+        input_audio_file=input_audio_file,
+        input_text=None,
+        source_language=None,
+        target_language=target_language,
+    )
+
+
+def process_t2st_example(
+    input_text: str, source_language: str, target_language: str
+) -> tuple[tuple[int, np.ndarray] | None, str]:
+    return api_predict(
+        task_name="T2ST",
+        audio_source="",
+        input_audio_mic=None,
+        input_audio_file=None,
+        input_text=input_text,
+        source_language=source_language,
+        target_language=target_language,
+    )
+
+
+def process_t2tt_example(
+    input_text: str, source_language: str, target_language: str
+) -> tuple[tuple[int, np.ndarray] | None, str]:
+    return api_predict(
+        task_name="T2TT",
+        audio_source="",
+        input_audio_mic=None,
+        input_audio_file=None,
+        input_text=input_text,
+        source_language=source_language,
+        target_language=target_language,
+    )
+
+
+def process_asr_example(
+    input_audio_file: str, target_language: str
+) -> tuple[tuple[int, np.ndarray] | None, str]:
+    return api_predict(
+        task_name="ASR",
+        audio_source="file",
+        input_audio_mic=None,
+        input_audio_file=input_audio_file,
+        input_text=None,
+        source_language=None,
+        target_language=target_language,
+    )
+
+
+def update_audio_ui(audio_source: str) -> tuple[dict, dict]:
+    mic = audio_source == "microphone"
+    return (
+        gr.update(visible=mic, value=None),  # input_audio_mic
+        gr.update(visible=not mic, value=None),  # input_audio_file
+    )
+
+
+def update_input_ui(task_name: str) -> tuple[dict, dict, dict, dict]:
+    task_name = task_name.split()[0]
+    if task_name == "S2ST":
+        return (
+            gr.update(visible=True),  # audio_box
+            gr.update(visible=False),  # input_text
+            gr.update(visible=False),  # source_language
+            gr.update(
+                visible=True,
+                choices=S2ST_TARGET_LANGUAGE_NAMES,
+                value=DEFAULT_TARGET_LANGUAGE,
+            ),  # target_language
+        )
+    elif task_name == "S2TT":
+        return (
+            gr.update(visible=True),  # audio_box
+            gr.update(visible=False),  # input_text
+            gr.update(visible=False),  # source_language
+            gr.update(
+                visible=True,
+                choices=S2TT_TARGET_LANGUAGE_NAMES,
+                value=DEFAULT_TARGET_LANGUAGE,
+            ),  # target_language
+        )
+    elif task_name == "T2ST":
+        return (
+            gr.update(visible=False),  # audio_box
+            gr.update(visible=True),  # input_text
+            gr.update(visible=True),  # source_language
+            gr.update(
+                visible=True,
+                choices=S2ST_TARGET_LANGUAGE_NAMES,
+                value=DEFAULT_TARGET_LANGUAGE,
+            ),  # target_language
+        )
+    elif task_name == "T2TT":
+        return (
+            gr.update(visible=False),  # audio_box
+            gr.update(visible=True),  # input_text
+            gr.update(visible=True),  # source_language
+            gr.update(
+                visible=True,
+                choices=T2TT_TARGET_LANGUAGE_NAMES,
+                value=DEFAULT_TARGET_LANGUAGE,
+            ),  # target_language
+        )
+    elif task_name == "ASR":
+        return (
+            gr.update(visible=True),  # audio_box
+            gr.update(visible=False),  # input_text
+            gr.update(visible=False),  # source_language
+            gr.update(
+                visible=True,
+                choices=S2TT_TARGET_LANGUAGE_NAMES,
+                value=DEFAULT_TARGET_LANGUAGE,
+            ),  # target_language
+        )
+    else:
+        raise ValueError(f"Unknown task: {task_name}")
+
+
+def update_output_ui(task_name: str) -> tuple[dict, dict]:
+    task_name = task_name.split()[0]
+    if task_name in ["S2ST", "T2ST"]:
+        return (
+            gr.update(visible=True, value=None),  # output_audio
+            gr.update(value=None),  # output_text
+        )
+    elif task_name in ["S2TT", "T2TT", "ASR"]:
+        return (
+            gr.update(visible=False, value=None),  # output_audio
+            gr.update(value=None),  # output_text
+        )
+    else:
+        raise ValueError(f"Unknown task: {task_name}")
+
+
+def update_example_ui(task_name: str) -> tuple[dict, dict, dict, dict, dict]:
+    task_name = task_name.split()[0]
+    return (
+        gr.update(visible=task_name == "S2ST"),  # s2st_example_row
+        gr.update(visible=task_name == "S2TT"),  # s2tt_example_row
+        gr.update(visible=task_name == "T2ST"),  # t2st_example_row
+        gr.update(visible=task_name == "T2TT"),  # t2tt_example_row
+        gr.update(visible=task_name == "ASR"),  # asr_example_row
+    )
+
+
+css = """
+h1 {
+  text-align: center;
+}
+
+#.contain {
+#  max-width: 730px;
+#  margin: auto;
+#  padding-top: 1.5rem;
+#}
+"""
+
+with gr.Blocks(css=css) as demo:
+    gr.Markdown(DESCRIPTION)
+    with gr.Group():
+        task_name = gr.Dropdown(
+            label="Task",
+            choices=TASK_NAMES,
+            value=TASK_NAMES[0],
+        )
+        with gr.Row():
+            source_language = gr.Dropdown(
+                label="Source language",
+                choices=TEXT_SOURCE_LANGUAGE_NAMES,
+                value="English",
+                visible=False,
+            )
+            target_language = gr.Dropdown(
+                label="Target language",
+                choices=S2ST_TARGET_LANGUAGE_NAMES,
+                value=DEFAULT_TARGET_LANGUAGE,
+            )
+        with gr.Row() as audio_box:
+            audio_source = gr.Radio(
+                label="Audio source",
+                choices=["file", "microphone"],
+                value="file",
+            )
+            input_audio_mic = gr.Audio(
+                label="Input speech",
+                type="filepath",
+                source="microphone",
+                visible=False,
+            )
+            input_audio_file = gr.Audio(
+                label="Input speech",
+                type="filepath",
+                source="upload",
+                visible=True,
+            )
+        input_text = gr.Textbox(label="Input text", visible=False)
+        with gr.Row():
+            btn = gr.Button("Translate")
+            btn_clean = gr.ClearButton([input_audio_mic, input_audio_file])
+        # gr.Markdown("## Text Examples")
+        with gr.Column():
+            output_audio = gr.Audio(
+                label="Translated speech",
+                autoplay=False,
+                streaming=False,
+                type="numpy",
+            )
+            output_text = gr.Textbox(label="Translated text")
+
+    with gr.Row(visible=True) as s2st_example_row:
+        s2st_examples = gr.Examples(
+            examples=[
+                ["assets/sample_input.mp3", "French"],
+                ["assets/sample_input.mp3", "Mandarin Chinese"],
+                ["assets/sample_input_2.mp3", "Hindi"],
+                ["assets/sample_input_2.mp3", "Spanish"],
+            ],
+            inputs=[input_audio_file, target_language],
+            outputs=[output_audio, output_text],
+            fn=process_s2st_example,
+        )
+    with gr.Row(visible=False) as s2tt_example_row:
+        s2tt_examples = gr.Examples(
+            examples=[
+                ["assets/sample_input.mp3", "French"],
+                ["assets/sample_input.mp3", "Mandarin Chinese"],
+                ["assets/sample_input_2.mp3", "Hindi"],
+                ["assets/sample_input_2.mp3", "Spanish"],
+            ],
+            inputs=[input_audio_file, target_language],
+            outputs=[output_audio, output_text],
+            fn=process_s2tt_example,
+        )
+    with gr.Row(visible=False) as t2st_example_row:
+        t2st_examples = gr.Examples(
+            examples=[
+                ["My favorite animal is the elephant.", "English", "French"],
+                ["My favorite animal is the elephant.", "English", "Mandarin Chinese"],
+                [
+                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
+                    "English",
+                    "Hindi",
+                ],
+                [
+                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
+                    "English",
+                    "Spanish",
+                ],
+            ],
+            inputs=[input_text, source_language, target_language],
+            outputs=[output_audio, output_text],
+            fn=process_t2st_example,
+        )
+    with gr.Row(visible=False) as t2tt_example_row:
+        t2tt_examples = gr.Examples(
+            examples=[
+                ["My favorite animal is the elephant.", "English", "French"],
+                ["My favorite animal is the elephant.", "English", "Mandarin Chinese"],
+                [
+                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
+                    "English",
+                    "Hindi",
+                ],
+                [
+                    "Meta AI's Seamless M4T model is democratising spoken communication across language barriers",
+                    "English",
+                    "Spanish",
+                ],
+            ],
+            inputs=[input_text, source_language, target_language],
+            outputs=[output_audio, output_text],
+            fn=process_t2tt_example,
+        )
+    with gr.Row(visible=False) as asr_example_row:
+        asr_examples = gr.Examples(
+            examples=[
+                ["assets/sample_input.mp3", "English"],
+                ["assets/sample_input_2.mp3", "English"],
+            ],
+            inputs=[input_audio_file, target_language],
+            outputs=[output_audio, output_text],
+            fn=process_asr_example,
+        )
+
+    audio_source.change(
+        fn=update_audio_ui,
+        inputs=audio_source,
+        outputs=[
+            input_audio_mic,
+            input_audio_file,
+        ],
+        queue=False,
+        api_name=False,
+    )
+    task_name.change(
+        fn=update_input_ui,
+        inputs=task_name,
+        outputs=[
+            audio_box,
+            input_text,
+            source_language,
+            target_language,
+        ],
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=update_output_ui,
+        inputs=task_name,
+        outputs=[output_audio, output_text],
+        queue=False,
+        api_name=False,
+    ).then(
+        fn=update_example_ui,
+        inputs=task_name,
+        outputs=[
+            s2st_example_row,
+            s2tt_example_row,
+            t2st_example_row,
+            t2tt_example_row,
+            asr_example_row,
+        ],
+        queue=False,
+        api_name=False,
+    )
+
+    btn.click(
+        fn=api_predict,
+        inputs=[
+            task_name,
+            audio_source,
+            input_audio_mic,
+            input_audio_file,
+            input_text,
+            source_language,
+            target_language,
+        ],
+        outputs=[output_audio, output_text],
+        api_name="run",
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch()