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0.2.15

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Quentin Fuxa
2025-11-21 23:52:00 +01:00
parent b5067249c0
commit 6206fff118
9 changed files with 75 additions and 20 deletions
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2
README.md
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@@ -141,7 +141,7 @@ async def websocket_endpoint(websocket: WebSocket):
|-----------|-------------|---------|
| `--model` | Whisper model size. List and recommandations [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/docs/available_models.md) | `small` |
| `--model-path` | Local .pt file/directory **or** Hugging Face repo ID containing the Whisper model. Overrides `--model`. Recommandations [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/docs/models_compatible_formats.md) | `None` |
| `--language` | List [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/simul_whisper/whisper/tokenizer.py). If you use `auto`, the model attempts to detect the language automatically, but it tends to bias towards English. | `auto` |
| `--language` | List [here](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/whisperlivekit/whisper/tokenizer.py). If you use `auto`, the model attempts to detect the language automatically, but it tends to bias towards English. | `auto` |
| `--target-language` | If sets, translates using [NLLW](https://github.com/QuentinFuxa/NoLanguageLeftWaiting). [200 languages available](https://github.com/QuentinFuxa/WhisperLiveKit/blob/main/docs/supported_languages.md). If you want to translate to english, you can also use `--direct-english-translation`. The STT model will try to directly output the translation. | `None` |
| `--diarization` | Enable speaker identification | `False` |
| `--backend-policy` | Streaming strategy: `1`/`simulstreaming` uses AlignAtt SimulStreaming, `2`/`localagreement` uses the LocalAgreement policy | `simulstreaming` |

2
docs/alignement_principles.md
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@@ -4,7 +4,7 @@
- Example 2: The punctuation from STT comes from prediction `t`, but the speaker change from Diariation come in the prediction `t-1`
- Example 3: The punctuation from STT comes from prediction `t-1`, but the speaker change from Diariation come in the prediction `t`
> `#` Is the split between the `t-1` prediction and t prediction.
> `#` Is the split between the `t-1` prediction and `t` prediction.
## Example 1:

43
docs/technical_integration.md Normal file
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@@ -0,0 +1,43 @@
# Technical Integration Guide
This document introduce how to reuse the core components when you do **not** want to ship the bundled frontend, FastAPI server, or even the provided CLI.
---
## 1. Runtime Components
| Layer | File(s) | Purpose |
|-------|---------|---------|
| Transport | `whisperlivekit/basic_server.py`, any ASGI/WebSocket server | Accepts audio over WebSocket (MediaRecorder WebM or raw PCM chunks) and streams JSON updates back |
| Audio processing | `whisperlivekit/audio_processor.py` | Buffers audio, orchestrates transcription, diarization, translation, handles FFmpeg/PCM input |
| Engines | `whisperlivekit/core.py`, `whisperlivekit/simul_whisper/*`, `whisperlivekit/local_agreement/*` | Load models once (SimulStreaming or LocalAgreement), expose `TranscriptionEngine` and helpers |
| Frontends | `whisperlivekit/web/*`, `chrome-extension/*` | Optional UI layers feeding the WebSocket endpoint |
**Key idea:** The server boundary is just `AudioProcessor.process_audio()` for incoming bytes and the async generator returned by `AudioProcessor.create_tasks()` for outgoing updates (`FrontData`). Everything else is optional.
---
## 2. Running Without the Bundled Frontend
1. Start the server/engine however you like:
```bash
wlk --model small --language en --host 0.0.0.0 --port 9000
# or launch your own app that instantiates TranscriptionEngine(...)
```
2. Build your own client (browser, mobile, desktop) that:
- Opens `ws(s)://<host>:<port>/asr`
- Sends either MediaRecorder/Opus WebM blobs **or** raw PCM (`--pcm-input` on the server tells the client to use the AudioWorklet).
- Consumes the JSON payload defined in `docs/API.md`.
---
## 3. Running Without FastAPI
`whisperlivekit/basic_server.py` is just an example. Any async framework works, as long as you:
1. Create a global `TranscriptionEngine` (expensive to initialize; reuse it).
2. Instantiate `AudioProcessor(transcription_engine=engine)` for each connection.
3. Call `create_tasks()` to get the async generator, `process_audio()` with incoming bytes, and ensure `cleanup()` runs when the client disconnects.
If you prefer to send compressed audio, instantiate `AudioProcessor(pcm_input=False)` and pipe encoded chunks through `FFmpegManager` transparently—just ensure `ffmpeg` is available or be ready to handle the `"ffmpeg_not_found"` error in the streamed `FrontData`.

2
pyproject.toml
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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "whisperlivekit"
version = "0.2.14.post4"
version = "0.2.15"
description = "Real-time speech-to-text with speaker diarization using Whisper"
readme = "README.md"
authors = [

3
whisperlivekit/local_agreement/backends.py
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@@ -224,7 +224,8 @@ class MLXWhisper(ASRBase):
if segment.get("no_speech_prob", 0) > 0.9:
continue
for word in segment.get("words", []):
token = ASRToken(word["start"], word["end"], word["word"], probability=word["probability"])
probability=word["probability"]
token = ASRToken(word["start"], word["end"], word["word"])
tokens.append(token)
return tokens

4
whisperlivekit/local_agreement/online_asr.py
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@@ -411,11 +411,11 @@ class OnlineASRProcessor:
) -> Transcript:
sep = sep if sep is not None else self.asr.sep
text = sep.join(token.text for token in tokens)
probability = sum(token.probability for token in tokens if token.probability) / len(tokens) if tokens else None
# probability = sum(token.probability for token in tokens if token.probability) / len(tokens) if tokens else None
if tokens:
start = offset + tokens[0].start
end = offset + tokens[-1].end
else:
start = None
end = None
return Transcript(start, end, text, probability=probability)
return Transcript(start, end, text)

2
whisperlivekit/simul_whisper/simul_whisper.py
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@@ -266,7 +266,7 @@ class AlignAtt:
logger.debug("Refreshing segment:")
self.init_tokens()
self.last_attend_frame = -self.cfg.rewind_threshold
self.detected_language = None
# self.detected_language = None
self.cumulative_time_offset = 0.0
self.init_context()
logger.debug(f"Context: {self.context}")

22
whisperlivekit/timed_objects.py
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@@ -19,8 +19,8 @@ class TimedText(Timed):
speaker: Optional[int] = -1
detected_language: Optional[str] = None
def is_punctuation(self) -> bool:
return self.text.strip() in PUNCTUATION_MARKS
def has_punctuation(self) -> bool:
return any(char in PUNCTUATION_MARKS for char in self.text.strip())
def is_within(self, other: 'TimedText') -> bool:
return other.contains_timespan(self)
@@ -65,6 +65,7 @@ class Transcript(TimedText):
sep: Optional[str] = None,
offset: float = 0
) -> "Transcript":
"""Collapse multiple ASR tokens into a single transcript span."""
sep = sep if sep is not None else ' '
text = sep.join(token.text for token in tokens)
if tokens:
@@ -107,18 +108,19 @@ class Silence():
@dataclass
class Segment():
class Segment(TimedText):
"""Generic contiguous span built from tokens or silence markers."""
start: Optional[float]
end: Optional[float]
text: Optional[str]
speaker: Optional[str]
@classmethod
def from_tokens(
cls,
tokens: List[Union[ASRToken, Silence]],
is_silence: bool = False
) -> Optional["Segment"]:
"""Return a normalized segment representing the provided tokens."""
if not tokens:
return None
@@ -129,16 +131,18 @@ class Segment():
start=start_token.start,
end=end_token.end,
text=None,
speaker = -2
speaker=-2
)
else:
return cls(
start=start_token.start,
end=end_token.end,
text=''.join(token.text for token in tokens),
speaker = -1
speaker=-1,
detected_language=start_token.detected_language
)
def is_silence(self) -> bool:
"""True when this segment represents a silence gap."""
return self.speaker == -2
@@ -147,6 +151,7 @@ class Line(TimedText):
translation: str = ''
def to_dict(self) -> Dict[str, Any]:
"""Serialize the line for frontend consumption."""
_dict: Dict[str, Any] = {
'speaker': int(self.speaker) if self.speaker != -1 else 1,
'text': self.text,
@@ -160,17 +165,21 @@ class Line(TimedText):
return _dict
def build_from_tokens(self, tokens: List[ASRToken]) -> "Line":
"""Populate line attributes from a contiguous token list."""
self.text = ''.join([token.text for token in tokens])
self.start = tokens[0].start
self.end = tokens[-1].end
self.speaker = 1
self.detected_language = tokens[0].detected_language
return self
def build_from_segment(self, segment: Segment) -> "Line":
"""Populate the line fields from a pre-built segment."""
self.text = segment.text
self.start = segment.start
self.end = segment.end
self.speaker = segment.speaker
self.detected_language = segment.detected_language
return self
def is_silent(self) -> bool:
@@ -195,6 +204,7 @@ class FrontData():
remaining_time_diarization: float = 0.
def to_dict(self) -> Dict[str, Any]:
"""Serialize the front-end data payload."""
_dict: Dict[str, Any] = {
'status': self.status,
'lines': [line.to_dict() for line in self.lines if (line.text or line.speaker == -2)],

15
whisperlivekit/tokens_alignment.py
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@@ -26,6 +26,7 @@ class TokensAlignment:
self.beg_loop: Optional[float] = None
def update(self) -> None:
"""Drain state buffers into the running alignment context."""
self.new_tokens, self.state.new_tokens = self.state.new_tokens, []
self.new_diarization, self.state.new_diarization = self.state.new_diarization, []
self.new_translation, self.state.new_translation = self.state.new_translation, []
@@ -37,6 +38,7 @@ class TokensAlignment:
self.new_translation_buffer = self.state.new_translation_buffer
def add_translation(self, line: Line) -> None:
"""Append translated text segments that overlap with a line."""
for ts in self.all_translation_segments:
if ts.is_within(line):
line.translation += ts.text + (self.sep if ts.text else '')
@@ -45,6 +47,7 @@ class TokensAlignment:
def compute_punctuations_segments(self, tokens: Optional[List[ASRToken]] = None) -> List[Segment]:
"""Group tokens into segments split by punctuation and explicit silence."""
segments = []
segment_start_idx = 0
for i, token in enumerate(self.all_tokens):
@@ -61,7 +64,7 @@ class TokensAlignment:
segments.append(segment)
segment_start_idx = i+1
else:
if token.is_punctuation():
if token.has_punctuation():
segment = Segment.from_tokens(
tokens=self.all_tokens[segment_start_idx: i+1],
)
@@ -77,6 +80,7 @@ class TokensAlignment:
def concatenate_diar_segments(self) -> List[SpeakerSegment]:
"""Merge consecutive diarization slices that share the same speaker."""
if not self.all_diarization_segments:
return []
merged = [self.all_diarization_segments[0]]
@@ -90,15 +94,14 @@ class TokensAlignment:
@staticmethod
def intersection_duration(seg1: TimedText, seg2: TimedText) -> float:
"""Return the overlap duration between two timed segments."""
start = max(seg1.start, seg2.start)
end = min(seg1.end, seg2.end)
return max(0, end - start)
def get_lines_diarization(self) -> Tuple[List[Line], str]:
"""
use compute_punctuations_segments, concatenate_diar_segments, intersection_duration
"""
"""Build lines when diarization is enabled and track overflow buffer."""
diarization_buffer = ''
punctuation_segments = self.compute_punctuations_segments()
diarization_segments = self.concatenate_diar_segments()
@@ -136,9 +139,7 @@ class TokensAlignment:
translation: bool = False,
current_silence: Optional[Silence] = None
) -> Tuple[List[Line], str, Union[str, TimedText]]:
"""
In the case without diarization
"""
"""Return the formatted lines plus buffers, optionally with diarization/translation."""
if diarization:
lines, diarization_buffer = self.get_lines_diarization()
else: