from simuleval.utils.agent import build_system_from_dir
from typing import Any, List, Optional, Tuple, Union
import numpy as np
import soundfile
import io
import asyncio
from simuleval.agents.pipeline import TreeAgentPipeline
from simuleval.agents.states import AgentStates
from simuleval.data.segments import Segment, EmptySegment, SpeechSegment
import threading
import math
import logging
import sys
from pathlib import Path
import time
from g2p_en import G2p
import torch
import traceback
import time
import random
import colorlog
from .speech_and_text_output import SpeechAndTextOutput
MODEL_SAMPLE_RATE = 16_000
logger = logging.getLogger(__name__)
# logger.propagate = False
handler = colorlog.StreamHandler(stream=sys.stdout)
formatter = colorlog.ColoredFormatter(
"%(log_color)s[%(asctime)s][%(levelname)s][%(module)s]:%(reset)s %(message)s",
reset=True,
log_colors={
"DEBUG": "cyan",
"INFO": "green",
"WARNING": "yellow",
"ERROR": "red",
"CRITICAL": "red,bg_white",
},
)
handler.setFormatter(formatter)
logger.addHandler(handler)
logger.setLevel(logging.WARNING)
class OutputSegments:
def __init__(self, segments: Union[List[Segment], Segment]):
if isinstance(segments, Segment):
segments = [segments]
self.segments: List[Segment] = [s for s in segments]
@property
def is_empty(self):
return all(segment.is_empty for segment in self.segments)
@property
def finished(self):
return all(segment.finished for segment in self.segments)
def compute_length(self, g2p):
lengths = []
for segment in self.segments:
if segment.data_type == "text":
lengths.append(len([x for x in g2p(segment.content) if x != " "]))
elif segment.data_type == "speech":
lengths.append(len(segment.content) / MODEL_SAMPLE_RATE)
elif isinstance(segment, EmptySegment):
continue
else:
logger.warning(
f"Unexpected data_type: {segment.data_type} not in 'speech', 'text'"
)
return max(lengths)
@classmethod
def join_output_buffer(
cls, buffer: List[List[Segment]], output: SpeechAndTextOutput
):
num_segments = len(buffer[0])
for i in range(num_segments):
segment_list = [
buffer[j][i]
for j in range(len(buffer))
if buffer[j][i].data_type is not None
]
if len(segment_list) == 0:
continue
if len(set(segment.data_type for segment in segment_list)) != 1:
logger.warning(
f"Data type mismatch at {i}: {set(segment.data_type for segment in segment_list)}"
)
continue
data_type = segment_list[0].data_type
if data_type == "text":
if output.text is not None:
logger.warning("Multiple text outputs, overwriting!")
output.text = " ".join([segment.content for segment in segment_list])
elif data_type == "speech":
if output.speech_samples is not None:
logger.warning("Multiple speech outputs, overwriting!")
speech_out = []
for segment in segment_list:
speech_out += segment.content
output.speech_samples = speech_out
output.speech_sample_rate = segment.sample_rate
elif isinstance(segment_list[0], EmptySegment):
continue
else:
logger.warning(
f"Invalid output buffer data type: {data_type}, expected 'speech' or 'text"
)
return output
def __repr__(self) -> str:
repr_str = str(self.segments)
return f"{self.__class__.__name__}(\n\t{repr_str}\n)"
class SimulevalTranscoder:
def __init__(self, agent, sample_rate, debug, buffer_limit):
self.agent = agent.agent
self.has_expressive = agent.has_expressive
self.input_queue = asyncio.Queue()
self.output_queue = asyncio.Queue()
self.states = self.agent.build_states()
if debug:
self.get_states_root().debug = True
self.incoming_sample_rate = sample_rate
self.close = False
self.g2p = G2p()
# buffer all outgoing translations within this amount of time
self.output_buffer_idle_ms = 5000
self.output_buffer_size_limit = (
buffer_limit # phonemes for text, seconds for speech
)
self.output_buffer_cur_size = 0
self.output_buffer: List[List[Segment]] = []
self.speech_output_sample_rate = None
self.last_output_ts = time.time() * 1000
self.timeout_ms = (
30000 # close the transcoder thread after this amount of silence
)
self.first_input_ts = None
self.first_output_ts = None
self.debug = debug
self.debug_ts = f"{time.time()}_{random.randint(1000, 9999)}"
if self.debug:
debug_folder = Path(__file__).resolve().parent.parent / "debug"
self.test_incoming_wav = soundfile.SoundFile(
debug_folder / f"{self.debug_ts}_test_incoming.wav",
mode="w+",
format="WAV",
subtype="PCM_16",
samplerate=self.incoming_sample_rate,
channels=1,
)
self.get_states_root().test_input_segments_wav = soundfile.SoundFile(
debug_folder / f"{self.debug_ts}_test_input_segments.wav",
mode="w+",
format="WAV",
samplerate=MODEL_SAMPLE_RATE,
channels=1,
)
def get_states_root(self) -> AgentStates:
if isinstance(self.agent, TreeAgentPipeline):
# self.states is a dict
return self.states[self.agent.source_module]
else:
# self.states is a list
return self.states[0]
def reset_states(self):
if isinstance(self.agent, TreeAgentPipeline):
states_iter = self.states.values()
else:
states_iter = self.states
for state in states_iter:
state.reset()
def debug_log(self, *args):
if self.debug:
logger.info(*args)
@classmethod
def build_agent(cls, model_path, config_name):
logger.info(f"Building simuleval agent: {model_path}, {config_name}")
agent = build_system_from_dir(
Path(__file__).resolve().parent.parent / f"models/{model_path}",
config_name=config_name,
)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
agent.to(device, fp16=True)
logger.info(
f"Successfully built simuleval agent {model_path} on device {device}"
)
return agent
def process_incoming_bytes(self, incoming_bytes, dynamic_config):
# TODO: We probably want to do some validation on dynamic_config to ensure it has what we needs
segment, sr = self._preprocess_wav(incoming_bytes)
segment = SpeechSegment(
content=segment,
sample_rate=sr,
tgt_lang=dynamic_config.get("targetLanguage"),
config=dynamic_config,
)
if dynamic_config.get("expressive") is True and self.has_expressive is False:
logger.warning(
"Passing 'expressive' but the agent does not support expressive output!"
)
# # segment is array([0, 0, 0, ..., 0, 0, 0], dtype=int16)
self.input_queue.put_nowait(segment)
def get_input_segment(self):
if self.input_queue.empty():
return None
chunk = self.input_queue.get_nowait()
self.input_queue.task_done()
return chunk
def convert_waveform(
self,
waveform: Union[np.ndarray, torch.Tensor],
sample_rate: int,
normalize_volume: bool = False,
to_mono: bool = False,
to_sample_rate: Optional[int] = None,
) -> Tuple[Union[np.ndarray, torch.Tensor], int]:
"""convert a waveform:
- to a target sample rate
- from multi-channel to mono channel
- volume normalization
Args:
waveform (numpy.ndarray or torch.Tensor): 2D original waveform
(channels x length)
sample_rate (int): original sample rate
normalize_volume (bool): perform volume normalization
to_mono (bool): convert to mono channel if having multiple channels
to_sample_rate (Optional[int]): target sample rate
Returns:
waveform (numpy.ndarray): converted 2D waveform (channels x length)
sample_rate (float): target sample rate
"""
try:
import torchaudio.sox_effects as ta_sox
except ImportError:
raise ImportError("Please install torchaudio: pip install torchaudio")
effects = []
if normalize_volume:
effects.append(["gain", "-n"])
if to_sample_rate is not None and to_sample_rate != sample_rate:
effects.append(["rate", f"{to_sample_rate}"])
if to_mono and waveform.shape[0] > 1:
effects.append(["channels", "1"])
if len(effects) > 0:
is_np_input = isinstance(waveform, np.ndarray)
_waveform = torch.from_numpy(waveform) if is_np_input else waveform
converted, converted_sample_rate = ta_sox.apply_effects_tensor(
_waveform, sample_rate, effects
)
if is_np_input:
converted = converted.numpy()
return converted, converted_sample_rate
return waveform, sample_rate
def _preprocess_wav(self, data: Any) -> Tuple[np.ndarray, int]:
segment, sample_rate = soundfile.read(
io.BytesIO(data),
dtype="float32",
always_2d=True,
frames=-1,
start=0,
format="RAW",
subtype="PCM_16",
samplerate=self.incoming_sample_rate,
channels=1,
)
if self.debug:
self.test_incoming_wav.seek(0, soundfile.SEEK_END)
self.test_incoming_wav.write(segment)
segment = segment.T
segment, new_sample_rate = self.convert_waveform(
segment,
sample_rate,
normalize_volume=False,
to_mono=True,
to_sample_rate=MODEL_SAMPLE_RATE,
)
assert MODEL_SAMPLE_RATE == new_sample_rate
segment = segment.squeeze(axis=0)
return segment, new_sample_rate
def process_pipeline_impl(self, input_segment):
try:
with torch.no_grad():
output_segment = OutputSegments(
self.agent.pushpop(input_segment, self.states)
)
if (
self.get_states_root().first_input_ts is not None
and self.first_input_ts is None
):
# TODO: this is hacky
self.first_input_ts = self.get_states_root().first_input_ts
if not output_segment.is_empty:
self.output_queue.put_nowait(output_segment)
if output_segment.finished:
self.debug_log("OUTPUT SEGMENT IS FINISHED. Resetting states.")
self.reset_states()
if self.debug:
# when we rebuild states, this value is reset to whatever
# is in the system dir config, which defaults debug=False.
self.get_states_root().debug = True
except Exception as e:
logger.error(f"Got exception while processing pipeline: {e}")
traceback.print_exc()
return input_segment
def process_pipeline_loop(self):
if self.close:
return # closes the thread
self.debug_log("processing_pipeline")
while not self.close:
input_segment = self.get_input_segment()
if input_segment is None:
if self.get_states_root().is_fresh_state: # TODO: this is hacky
time.sleep(0.3)
else:
time.sleep(0.03)
continue
self.process_pipeline_impl(input_segment)
self.debug_log("finished processing_pipeline")
def process_pipeline_once(self):
if self.close:
return
self.debug_log("processing pipeline once")
input_segment = self.get_input_segment()
if input_segment is None:
return
self.process_pipeline_impl(input_segment)
self.debug_log("finished processing_pipeline_once")
def get_output_segment(self):
if self.output_queue.empty():
return None
output_chunk = self.output_queue.get_nowait()
self.output_queue.task_done()
return output_chunk
def start(self):
self.debug_log("starting transcoder in a thread")
threading.Thread(target=self.process_pipeline_loop).start()
def first_translation_time(self):
return round((self.first_output_ts - self.first_input_ts) / 1000, 2)
def get_buffered_output(self) -> SpeechAndTextOutput:
now = time.time() * 1000
self.debug_log(f"get_buffered_output queue size: {self.output_queue.qsize()}")
while not self.output_queue.empty():
tmp_out = self.get_output_segment()
if tmp_out and tmp_out.compute_length(self.g2p) > 0:
if len(self.output_buffer) == 0:
self.last_output_ts = now
self._populate_output_buffer(tmp_out)
self._increment_output_buffer_size(tmp_out)
if tmp_out.finished:
self.debug_log("tmp_out.finished")
res = self._gather_output_buffer_data(final=True)
self.debug_log(f"gathered output data: {res}")
self.output_buffer = []
self.increment_output_buffer_size = 0
self.last_output_ts = now
self.first_output_ts = now
return res
else:
self.debug_log("tmp_out.compute_length is not > 0")
if len(self.output_buffer) > 0 and (
now - self.last_output_ts >= self.output_buffer_idle_ms
or self.output_buffer_cur_size >= self.output_buffer_size_limit
):
self.debug_log(
"[get_buffered_output] output_buffer is not empty. getting res to return."
)
self.last_output_ts = now
res = self._gather_output_buffer_data(final=False)
self.debug_log(f"gathered output data: {res}")
self.output_buffer = []
self.output_buffer_phoneme_count = 0
self.first_output_ts = now
return res
else:
self.debug_log("[get_buffered_output] output_buffer is empty...")
return None
def _gather_output_buffer_data(self, final):
output = SpeechAndTextOutput()
output.final = final
output = OutputSegments.join_output_buffer(self.output_buffer, output)
return output
def _increment_output_buffer_size(self, segment: OutputSegments):
self.output_buffer_cur_size += segment.compute_length(self.g2p)
def _populate_output_buffer(self, segment: OutputSegments):
self.output_buffer.append(segment.segments)
def _compute_phoneme_count(self, string: str) -> int:
return len([x for x in self.g2p(string) if x != " "])