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from __future__ import annotations
import os
import gradio as gr
import numpy as np
import torch
import torchaudio
from seamless_communication.models.inference.translator 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,
)
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"
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"
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
translator = Translator(
model_name_or_card="seamlessM4T_large",
vocoder_name_or_card="vocoder_36langs",
device=device,
sample_rate=AUDIO_SAMPLE_RATE,
)
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
text_out, wav, sr = translator.predict(
input=input_data,
task_str=task_name,
tgt_lang=target_language_code,
src_lang=source_language_code,
ngram_filtering=True,
)
if task_name in ["S2ST", "T2ST"]:
return (sr, wav.cpu().detach().numpy()), text_out
else:
return None, text_out
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",
)
demo.queue(max_size=50).launch()
# Linking models to the space
# 'facebook/seamless-m4t-large'
# 'facebook/SONAR'
|