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from __future__ import annotations | |
import os | |
import gradio as gr | |
import numpy as np | |
import torch | |
import torchaudio | |
from huggingface_hub import snapshot_download | |
from seamless_communication.inference import Translator | |
from lang_list import ( | |
LANGUAGE_NAME_TO_CODE, | |
S2ST_TARGET_LANGUAGE_NAMES, | |
S2TT_TARGET_LANGUAGE_NAMES, | |
T2TT_TARGET_LANGUAGE_NAMES, | |
TEXT_SOURCE_LANGUAGE_NAMES, | |
) | |
snapshot_download(repo_id="meta-private/M4Tv2", repo_type="model", local_dir="models") | |
DESCRIPTION = """# SeamlessM4T | |
[SeamlessM4T](https://github.com/facebookresearch/seamless_communication) is designed to provide high-quality | |
translation, allowing people from different linguistic communities to communicate effortlessly through speech and text. | |
This unified model enables multiple tasks like Speech-to-Speech (S2ST), Speech-to-Text (S2TT), Text-to-Speech (T2ST) | |
translation and more, without relying on multiple separate models. | |
""" | |
CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1" and torch.cuda.is_available() | |
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)", | |
] | |
AUDIO_SAMPLE_RATE = 16000.0 | |
MAX_INPUT_AUDIO_LENGTH = 60 # in seconds | |
DEFAULT_TARGET_LANGUAGE = "French" | |
if torch.cuda.is_available(): | |
device = torch.device("cuda:0") | |
dtype = torch.float16 | |
else: | |
device = torch.device("cpu") | |
dtype = torch.float32 | |
translator = Translator( | |
model_name_or_card="seamlessM4T_v2_large", | |
vocoder_name_or_card="vocoder_v2", | |
device=device, | |
dtype=dtype, | |
) | |
def 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, | |
) -> tuple[tuple[int, np.ndarray] | None, str]: | |
task_name = task_name.split()[0] | |
source_language_code = LANGUAGE_NAME_TO_CODE[source_language] if source_language else None | |
target_language_code = LANGUAGE_NAME_TO_CODE[target_language] | |
if task_name in ["S2ST", "S2TT", "ASR"]: | |
if audio_source == "microphone": | |
input_data = input_audio_mic | |
else: | |
input_data = input_audio_file | |
arr, org_sr = torchaudio.load(input_data) | |
new_arr = torchaudio.functional.resample(arr, orig_freq=org_sr, new_freq=AUDIO_SAMPLE_RATE) | |
max_length = int(MAX_INPUT_AUDIO_LENGTH * AUDIO_SAMPLE_RATE) | |
if new_arr.shape[1] > max_length: | |
new_arr = new_arr[:, :max_length] | |
gr.Warning(f"Input audio is too long. Only the first {MAX_INPUT_AUDIO_LENGTH} seconds is used.") | |
torchaudio.save(input_data, new_arr, sample_rate=int(AUDIO_SAMPLE_RATE)) | |
else: | |
input_data = input_text | |
out_texts, out_audios = translator.predict( | |
input=input_data, | |
task_str=task_name, | |
tgt_lang=target_language_code, | |
src_lang=source_language_code, | |
) | |
out_text = str(out_texts[0]) | |
if task_name in ["S2ST", "T2ST"]: | |
out_wav = out_audios.audio_wavs[0] | |
return (int(AUDIO_SAMPLE_RATE), out_wav.cpu().detach().numpy()), out_text | |
else: | |
return None, out_text | |
def process_s2st_example(input_audio_file: str, target_language: str) -> tuple[tuple[int, np.ndarray] | None, str]: | |
return 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 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 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 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 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 | |
) | |
with gr.Blocks(css="style.css") as demo: | |
gr.Markdown(DESCRIPTION) | |
gr.DuplicateButton( | |
value="Duplicate Space for private use", | |
elem_id="duplicate-button", | |
visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", | |
) | |
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) | |
btn = gr.Button("Translate") | |
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, | |
cache_examples=CACHE_EXAMPLES, | |
) | |
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, | |
cache_examples=CACHE_EXAMPLES, | |
) | |
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, | |
cache_examples=CACHE_EXAMPLES, | |
) | |
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, | |
cache_examples=CACHE_EXAMPLES, | |
) | |
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, | |
cache_examples=CACHE_EXAMPLES, | |
) | |
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=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(max_size=50).launch() | |