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#!/usr/bin/env python
import os
import pathlib
import tempfile
import gradio as gr
import torch
import torchaudio
from fairseq2.assets import InProcAssetMetadataProvider, asset_store
from fairseq2.data import Collater, SequenceData, VocabularyInfo
from fairseq2.data.audio import (
AudioDecoder,
WaveformToFbankConverter,
WaveformToFbankOutput,
)
from seamless_communication.inference import SequenceGeneratorOptions
from fairseq2.generation import NGramRepeatBlockProcessor
from fairseq2.memory import MemoryBlock
from fairseq2.typing import DataType, Device
from huggingface_hub import snapshot_download
from seamless_communication.inference import BatchedSpeechOutput, Translator, SequenceGeneratorOptions
from seamless_communication.models.generator.loader import load_pretssel_vocoder_model
from seamless_communication.models.unity import (
UnitTokenizer,
load_gcmvn_stats,
load_unity_text_tokenizer,
load_unity_unit_tokenizer,
)
from torch.nn import Module
from seamless_communication.cli.expressivity.evaluate.pretssel_inference_helper import PretsselGenerator
from utils import LANGUAGE_CODE_TO_NAME
DESCRIPTION = """\
# Seamless Expressive
[SeamlessExpressive](https://github.com/facebookresearch/seamless_communication) is a speech-to-speech translation model that captures certain underexplored aspects of prosody such as speech rate and pauses, while preserving the style of one's voice and high content translation quality.
"""
CACHE_EXAMPLES = os.getenv("CACHE_EXAMPLES") == "1" and torch.cuda.is_available()
CHECKPOINTS_PATH = pathlib.Path(os.getenv("CHECKPOINTS_PATH", "/home/user/app/models"))
if not CHECKPOINTS_PATH.exists():
snapshot_download(repo_id="facebook/seamless-expressive", repo_type="model", local_dir=CHECKPOINTS_PATH)
snapshot_download(repo_id="facebook/seamless-m4t-v2-large", repo_type="model", local_dir=CHECKPOINTS_PATH)
# Ensure that we do not have any other environment resolvers and always return
# "demo" for demo purposes.
asset_store.env_resolvers.clear()
asset_store.env_resolvers.append(lambda: "demo")
# Construct an `InProcAssetMetadataProvider` with environment-specific metadata
# that just overrides the regular metadata for "demo" environment. Note the "@demo" suffix.
demo_metadata = [
{
"name": "seamless_expressivity@demo",
"checkpoint": f"file://{CHECKPOINTS_PATH}/m2m_expressive_unity.pt",
"char_tokenizer": f"file://{CHECKPOINTS_PATH}/spm_char_lang38_tc.model",
},
{
"name": "vocoder_pretssel@demo",
"checkpoint": f"file://{CHECKPOINTS_PATH}/pretssel_melhifigan_wm-final.pt",
},
{
"name": "seamlessM4T_v2_large@demo",
"checkpoint": f"file://{CHECKPOINTS_PATH}/seamlessM4T_v2_large.pt",
"char_tokenizer": f"file://{CHECKPOINTS_PATH}/spm_char_lang38_tc.model",
},
]
asset_store.metadata_providers.append(InProcAssetMetadataProvider(demo_metadata))
LANGUAGE_NAME_TO_CODE = {v: k for k, v in LANGUAGE_CODE_TO_NAME.items()}
if torch.cuda.is_available():
device = torch.device("cuda:0")
dtype = torch.float16
else:
device = torch.device("cpu")
dtype = torch.float32
MODEL_NAME = "seamless_expressivity"
VOCODER_NAME = "vocoder_pretssel"
# used for ASR for toxicity
m4t_translator = Translator(
model_name_or_card="seamlessM4T_v2_large",
vocoder_name_or_card=None,
device=device,
dtype=dtype,
)
unit_tokenizer = load_unity_unit_tokenizer(MODEL_NAME)
_gcmvn_mean, _gcmvn_std = load_gcmvn_stats(VOCODER_NAME)
gcmvn_mean = torch.tensor(_gcmvn_mean, device=device, dtype=dtype)
gcmvn_std = torch.tensor(_gcmvn_std, device=device, dtype=dtype)
translator = Translator(
MODEL_NAME,
vocoder_name_or_card=None,
device=device,
dtype=dtype,
apply_mintox=False,
)
text_generation_opts = SequenceGeneratorOptions(
beam_size=5,
unk_penalty=torch.inf,
soft_max_seq_len=(0, 200),
step_processor=NGramRepeatBlockProcessor(
ngram_size=10,
),
)
m4t_text_generation_opts = SequenceGeneratorOptions(
beam_size=5,
unk_penalty=torch.inf,
soft_max_seq_len=(1, 200),
step_processor=NGramRepeatBlockProcessor(
ngram_size=10,
),
)
pretssel_generator = PretsselGenerator(
VOCODER_NAME,
vocab_info=unit_tokenizer.vocab_info,
device=device,
dtype=dtype,
)
decode_audio = AudioDecoder(dtype=torch.float32, device=device)
convert_to_fbank = WaveformToFbankConverter(
num_mel_bins=80,
waveform_scale=2**15,
channel_last=True,
standardize=False,
device=device,
dtype=dtype,
)
def normalize_fbank(data: WaveformToFbankOutput) -> WaveformToFbankOutput:
fbank = data["fbank"]
std, mean = torch.std_mean(fbank, dim=0)
data["fbank"] = fbank.subtract(mean).divide(std)
data["gcmvn_fbank"] = fbank.subtract(gcmvn_mean).divide(gcmvn_std)
return data
collate = Collater(pad_value=0, pad_to_multiple=1)
AUDIO_SAMPLE_RATE = 16000
MAX_INPUT_AUDIO_LENGTH = 10 # in seconds
def remove_prosody_tokens_from_text(text):
# filter out prosody tokens, there is only emphasis '*', and pause '='
text = text.replace("*", "").replace("=", "")
text = " ".join(text.split())
return text
def preprocess_audio(input_audio_path: str) -> None:
arr, org_sr = torchaudio.load(input_audio_path)
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_audio_path, new_arr, sample_rate=AUDIO_SAMPLE_RATE)
def run(
input_audio_path: str,
source_language: str,
target_language: str,
) -> tuple[str, str]:
target_language_code = LANGUAGE_NAME_TO_CODE[target_language]
source_language_code = LANGUAGE_NAME_TO_CODE[source_language]
preprocess_audio(input_audio_path)
with pathlib.Path(input_audio_path).open("rb") as fb:
block = MemoryBlock(fb.read())
example = decode_audio(block)
example = convert_to_fbank(example)
example = normalize_fbank(example)
example = collate(example)
# get transcription for mintox
source_sentences, _ = m4t_translator.predict(
input=example["fbank"],
task_str="S2TT", # get source text
tgt_lang=source_language_code,
text_generation_opts=m4t_text_generation_opts,
)
source_text = str(source_sentences[0])
prosody_encoder_input = example["gcmvn_fbank"]
text_output, unit_output = translator.predict(
example["fbank"],
"S2ST",
tgt_lang=target_language_code,
src_lang=source_language_code,
text_generation_opts=text_generation_opts,
unit_generation_ngram_filtering=False,
duration_factor=1.0,
prosody_encoder_input=prosody_encoder_input,
src_text=source_text, # for mintox check
)
speech_output = pretssel_generator.predict(
unit_output.units,
tgt_lang=target_language_code,
prosody_encoder_input=prosody_encoder_input,
)
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
torchaudio.save(
f.name,
speech_output.audio_wavs[0][0].to(torch.float32).cpu(),
sample_rate=speech_output.sample_rate,
)
text_out = remove_prosody_tokens_from_text(str(text_output[0]))
return f.name, text_out
TARGET_LANGUAGE_NAMES = [
"English",
"French",
"German",
"Spanish",
]
UPDATED_LANGUAGE_LIST = {
"English": ["French", "German", "Spanish"],
"French": ["English", "German", "Spanish"],
"German": ["English", "French", "Spanish"],
"Spanish": ["English", "French", "German"],
}
def rs_change(rs):
return gr.update(choices=UPDATED_LANGUAGE_LIST[rs], value=None)
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.Row():
with gr.Column():
with gr.Group():
input_audio = gr.Audio(label="Input speech", type="filepath")
source_language = gr.Dropdown(
label="Source language",
choices=TARGET_LANGUAGE_NAMES,
value="English",
)
target_language = gr.Dropdown(
label="Target language",
choices=TARGET_LANGUAGE_NAMES,
value="French",
)
source_language.change(
fn=rs_change,
inputs=[source_language],
outputs=[target_language],
)
btn = gr.Button()
with gr.Column():
with gr.Group():
output_audio = gr.Audio(label="Translated speech")
output_text = gr.Textbox(label="Translated text")
gr.Examples(
examples=[
["assets/Excited-Es.wav", "English", "Spanish"],
["assets/FastTalking-En.wav", "French", "English"],
["assets/Sad-Es.wav", "English", "Spanish"],
],
inputs=[input_audio, source_language, target_language],
outputs=[output_audio, output_text],
fn=run,
cache_examples=CACHE_EXAMPLES,
api_name=False,
)
btn.click(
fn=run,
inputs=[input_audio, source_language, target_language],
outputs=[output_audio, output_text],
api_name="run",
)
if __name__ == "__main__":
demo.queue(max_size=50).launch()
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