Speech-to-Speech Implementation for Simultaneous Interpreting Simultaneous interpreting – conferences, international negotiations, live broadcasts. The requirements are strict: a delay of no more than 3–5 seconds, high terminology accuracy, maintaining the speaker's speech tempo. ### Simultaneous Interpreting Architecture
[Speaker] → WebRTC → VAD → Chunker → STT → MT → TTS → [Listeners]
↓ sliding window (2–4 сек)
```Key engineering solutions: - **Sliding window**: we don't wait for the end of a sentence, we translate with a sliding window of 2-4 seconds - **Anticipation**: we start TTS before the translation is complete - **Speed normalization**: we speed up TTS if the translated speech is longer than the source ### Sliding window transcription```python
import asyncio
from collections import deque
class SynchronousTranslator:
def __init__(self, window_sec: float = 3.0, step_sec: float = 1.0):
self.window = window_sec
self.step = step_sec
self.audio_buffer = deque()
self.sample_rate = 16000
async def process_stream(self, audio_generator):
"""Обрабатываем аудио скользящим окном"""
window_samples = int(self.window * self.sample_rate)
step_samples = int(self.step * self.sample_rate)
async for chunk in audio_generator:
self.audio_buffer.extend(chunk)
if len(self.audio_buffer) >= window_samples:
window_audio = list(self.audio_buffer)[:window_samples]
# Сдвигаем буфер на step
for _ in range(step_samples):
if self.audio_buffer:
self.audio_buffer.popleft()
# Транскрибируем и переводим
yield await self.translate_chunk(bytes(window_audio))
```### Speech rate adaptation```python
from pydub import AudioSegment, effects
def adapt_speech_speed(audio: bytes, target_duration_sec: float) -> bytes:
"""Ускоряем/замедляем TTS под темп оригинала"""
segment = AudioSegment.from_wav(io.BytesIO(audio))
current_duration = len(segment) / 1000
if current_duration == 0:
return audio
speed_factor = current_duration / target_duration_sec
speed_factor = max(0.7, min(1.5, speed_factor)) # ограничиваем 0.7–1.5x
# Изменение скорости без изменения питча
adjusted = effects.speedup(segment, playback_speed=speed_factor)
output = io.BytesIO()
adjusted.export(output, format="wav")
return output.getvalue()
```### Specifics for negotiations - Preloading a terminological dictionary for the negotiation domain - List of participant names for correct recognition - Boosting key terms in STT - Prompt for MT with context: industry, type of meeting Timeframe: MVP of the simultaneous interpretation system: 4-6 weeks. Low-latency production: 2-3 months.