mirror of
https://github.com/QuentinFuxa/WhisperLiveKit.git
synced 2026-03-07 22:33:36 +00:00
8c799fa4d1
fixes #283, fixes #275 - accumulated_cross_attns was growing unboundedly during decoding loop, using up to ~5GB for repetition loops. now capped to rolling window of 16 - max_tokens_per_chunk was using TOKENS_PER_SECOND (mel frame rate = 50) instead of actual text token rate (~15/s), allowing 10-40x too many decoding steps - removed unused torch.cat on early return path - removed dead self.committed/last_result_tokens lists (never read) - same fixes applied to mlx variant
757 lines
29 KiB
Python
757 lines
29 KiB
Python
"""
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MLX whisper AlignAtt streaming decoder
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"""
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import logging
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from time import time
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from typing import Any, List, Optional, Tuple
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import mlx.core as mx
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import numpy as np
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from mlx_whisper.audio import log_mel_spectrogram as mlx_log_mel_spectrogram
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from mlx_whisper.transcribe import pad_or_trim as mlx_pad_or_trim
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from whisperlivekit.timed_objects import ASRToken
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from whisperlivekit.whisper import DecodingOptions, tokenizer
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from whisperlivekit.whisper.audio import N_FRAMES, N_SAMPLES, TOKENS_PER_SECOND
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from ..config import AlignAttConfig
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from .decoder_state import MLXDecoderState
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from .decoders import MLXBeamSearchDecoder, MLXGreedyDecoder, MLXInference
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DEC_PAD = 50257
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logger = logging.getLogger(__name__)
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class MLXTokenBuffer: #should try to make it heritate from classic simul whisper class
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"""Token buffer for MLX-based decoding."""
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def __init__(self, text="", tokenizer=None, prefix_token_ids=None):
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self.text = text
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self.prefix_token_ids = prefix_token_ids or []
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self.tokenizer = tokenizer
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self.pending_token_ids = []
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def as_token_ids(self, tokenizer=None):
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if tokenizer is None:
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tokenizer = self.tokenizer
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if tokenizer is None:
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raise ValueError("Tokenizer is not set.")
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return self.prefix_token_ids + tokenizer.encode(self.text)
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def as_mlx_array(self) -> mx.array:
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"""Return tokens as MLX array."""
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tok_ids = self.as_token_ids()
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return mx.array([tok_ids], dtype=mx.int32)
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def as_mlx_array_beam(self, beam: int) -> mx.array:
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"""Return tokens as MLX array repeated for beam search."""
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t = self.as_mlx_array()
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return mx.repeat(t, beam, axis=0)
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def as_text(self):
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return self.text
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@staticmethod
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def empty(*a, **kw):
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return MLXTokenBuffer(*a, **kw)
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@staticmethod
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def from_text(text, *a, **kw):
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return MLXTokenBuffer(*a, text=text, **kw)
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def is_empty(self):
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return self.text is None or self.text == ""
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def trim_words(self, num=1, after=0):
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"""Trim words from the beginning of the context."""
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tokenizer = self.tokenizer
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assert tokenizer is not None, "Tokenizer is not set."
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ids = tokenizer.encode(self.text[after:])
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words, wids = self.tokenizer.split_to_word_tokens(ids)
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if not words:
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return 0
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self.text = self.text[:after] + "".join(words[num:])
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return sum(len(wi) for wi in wids[:num])
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def append_token_ids(self, token_ids):
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"""Append token IDs to the buffer, handling incomplete UTF-8."""
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tokenizer = self.tokenizer
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assert tokenizer is not None, "Tokenizer is not set."
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all_tokens = self.pending_token_ids + token_ids
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decoded = tokenizer.decode(all_tokens)
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replacement_char = "\ufffd"
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if replacement_char in decoded:
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if len(all_tokens) > 1:
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decoded_partial = tokenizer.decode(all_tokens[:-1])
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if replacement_char not in decoded_partial:
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self.text += decoded_partial
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self.pending_token_ids = [all_tokens[-1]]
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else:
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self.pending_token_ids = all_tokens
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else:
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self.pending_token_ids = all_tokens
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else:
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self.text += decoded
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self.pending_token_ids = []
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def mlx_median_filter(x: mx.array, filter_width: int) -> mx.array:
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"""
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Apply median filter along the last axis.
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Args:
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x: Input array of shape (..., T)
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filter_width: Width of the median filter (should be odd)
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Returns:
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Filtered array of same shape
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"""
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if filter_width <= 1:
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return x
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pad_width = filter_width // 2
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shape = x.shape
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left_pad = mx.repeat(x[..., :1], pad_width, axis=-1)
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right_pad = mx.repeat(x[..., -1:], pad_width, axis=-1)
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x_padded = mx.concatenate([left_pad, x, right_pad], axis=-1)
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result_shape = list(shape)
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result = []
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for i in range(shape[-1]):
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window = x_padded[..., i:i + filter_width]
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sorted_window = mx.sort(window, axis=-1)
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median_val = sorted_window[..., filter_width // 2:filter_width // 2 + 1]
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result.append(median_val)
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return mx.concatenate(result, axis=-1)
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class MLXAlignAtt:
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"""
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MLX-native Alignment-based Attention decoder for SimulStreaming.
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This class runs entirely on MLX, with no PyTorch dependencies for inference.
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"""
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@property
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def speaker(self):
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return self.state.speaker
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@speaker.setter
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def speaker(self, value):
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self.state.speaker = value
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@property
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def global_time_offset(self):
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return self.state.global_time_offset
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@global_time_offset.setter
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def global_time_offset(self, value):
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self.state.global_time_offset = value
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def __init__(
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self,
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cfg: AlignAttConfig,
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mlx_model: Any,
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) -> None:
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"""
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Initialize MLX AlignAtt decoder.
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Args:
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cfg: AlignAtt configuration
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mlx_model: MLX Whisper model (full model, not just encoder)
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"""
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self.model = mlx_model
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self.cfg = cfg
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logger.info(f"MLX Model dimensions: {self.model.dims}")
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self.decode_options = DecodingOptions(
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language=cfg.language,
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without_timestamps=True,
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task=cfg.task
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)
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self.tokenizer_is_multilingual = cfg.tokenizer_is_multilingual
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self.max_text_len = self.model.dims.n_text_ctx
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self.num_decoder_layers = len(self.model.decoder.blocks)
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if self.cfg.max_context_tokens is None:
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self.max_context_tokens = self.max_text_len
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else:
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self.max_context_tokens = self.cfg.max_context_tokens
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# Initialize per-session state
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self.state = MLXDecoderState()
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self._init_state(cfg)
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def _init_state(self, cfg: AlignAttConfig):
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"""Initialize the per-session decoder state."""
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self.create_tokenizer(cfg.language if cfg.language != "auto" else None)
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self.state.tokenizer = self.tokenizer
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self.state.detected_language = cfg.language if cfg.language != "auto" else None
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self.state.global_time_offset = 0.0
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self.state.last_attend_frame = -cfg.rewind_threshold
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self.state.speaker = -1
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if cfg.cif_ckpt_path is None or not cfg.cif_ckpt_path:
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if cfg.never_fire:
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self.state.never_fire = True
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self.state.always_fire = False
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else:
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self.state.always_fire = True
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self.state.never_fire = False
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else:
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logger.warning("CIF checkpoint provided but MLX CIF not implemented. Using always_fire=True")
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self.state.always_fire = True
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self.state.never_fire = cfg.never_fire
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self._build_alignment_source()
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suppress_tokens = [
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self.tokenizer.transcribe,
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self.tokenizer.translate,
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self.tokenizer.sot,
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self.tokenizer.sot_prev,
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self.tokenizer.sot_lm,
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self.tokenizer.no_timestamps,
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] + list(self.tokenizer.all_language_tokens)
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if self.tokenizer.no_speech is not None:
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suppress_tokens.append(self.tokenizer.no_speech)
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self.state.suppress_tokens = tuple(sorted(set(suppress_tokens)))
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logger.debug(f"Suppress tokens: {self.state.suppress_tokens}")
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self.init_tokens()
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self.init_context()
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self.state.decoder_type = cfg.decoder_type
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if cfg.decoder_type == "greedy":
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logger.info("Using MLX greedy decoder")
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self.state.token_decoder = MLXGreedyDecoder(0.0, self.tokenizer.eot)
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elif cfg.decoder_type == "beam":
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logger.info("Using MLX beam decoder")
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self.state.inference = MLXInference(self.model, self.state.initial_token_length)
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self.state.token_decoder = MLXBeamSearchDecoder(
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inference=self.state.inference,
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eot=self.tokenizer.eot,
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beam_size=cfg.beam_size
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)
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def _build_alignment_source(self):
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"""Build alignment source mapping from model's alignment_heads."""
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self.state.align_source = {}
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self.state.num_align_heads = 0
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alignment_heads = self.model.alignment_heads
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if alignment_heads is None:
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logger.warning("No alignment heads found in model")
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return
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if hasattr(alignment_heads, 'tolist'):
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heads_list = alignment_heads.tolist()
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else:
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heads_list = np.array(alignment_heads).tolist()
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for layer_rank, head_id in heads_list:
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layer_rank = int(layer_rank)
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head_id = int(head_id)
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heads = self.state.align_source.get(layer_rank, [])
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heads.append((self.state.num_align_heads, head_id))
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self.state.align_source[layer_rank] = heads
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self.state.num_align_heads += 1
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def warmup(self, audio: np.ndarray):
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"""Warmup the model with sample audio."""
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try:
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self.insert_audio(audio)
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self.infer(is_last=True)
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self.refresh_segment(complete=True)
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logger.info("MLX model warmed up successfully")
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except Exception as e:
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logger.exception(f"MLX model warmup failed: {e}")
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def create_tokenizer(self, language=None):
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"""Create tokenizer for the given language."""
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self.tokenizer = tokenizer.get_tokenizer(
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multilingual=self.tokenizer_is_multilingual,
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language=language,
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num_languages=self.model.num_languages,
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task=self.decode_options.task
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)
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self.state.tokenizer = self.tokenizer
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def init_context(self):
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"""Initialize context buffer."""
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kw = {
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'tokenizer': self.tokenizer,
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'prefix_token_ids': [self.tokenizer.sot_prev]
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}
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self.state.context = MLXTokenBuffer.empty(**kw)
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if self.cfg.static_init_prompt is not None:
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self.state.context = MLXTokenBuffer.from_text(self.cfg.static_init_prompt, **kw)
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if self.cfg.init_prompt is not None:
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self.state.context.text += self.cfg.init_prompt
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def init_tokens(self):
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"""Initialize token sequence."""
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logger.debug(f"init tokens, {len(self.state.segments)}")
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self.state.initial_tokens = mx.array(
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[self.tokenizer.sot_sequence_including_notimestamps],
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dtype=mx.int32
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)
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self.state.initial_token_length = self.state.initial_tokens.shape[1]
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self.state.sot_index = self.tokenizer.sot_sequence.index(self.tokenizer.sot)
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logger.debug(f"init tokens after, {len(self.state.segments)}")
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self.state.tokens = [self.state.initial_tokens]
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def trim_context(self):
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"""Trim context if too long."""
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logger.info("Trimming context")
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c = len(self.state.context.as_token_ids()) - len(self.state.context.prefix_token_ids)
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logger.info(f"Context text: {self.state.context.as_text()}")
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l = sum(t.shape[1] for t in self.state.tokens) + c
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if self.cfg.static_init_prompt is None:
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after = 0
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else:
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after = len(self.cfg.static_init_prompt)
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while c > self.max_context_tokens or l > self.max_text_len - 20:
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t = self.state.context.trim_words(after=after)
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l -= t
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c -= t
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logger.debug(f"len {l}, c {c}, max_context_tokens {self.max_context_tokens}")
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if t == 0:
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break
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logger.info(f"Context after trim: {self.state.context.text} (len: {l})")
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def refresh_segment(self, complete=False):
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"""Refresh segment state."""
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logger.debug("Refreshing segment:")
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self.init_tokens()
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self.state.last_attend_frame = -self.cfg.rewind_threshold
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self.state.cumulative_time_offset = 0.0
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self.init_context()
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logger.debug(f"Context: {self.state.context}")
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if not complete and len(self.state.segments) > 2:
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self.state.segments = self.state.segments[-2:]
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else:
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logger.debug("removing all segments.")
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self.state.segments = []
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self.state.log_segments += 1
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self.state.pending_incomplete_tokens = []
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def fire_at_boundary(self, chunked_encoder_feature: mx.array) -> bool:
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"""Check if we should fire at word boundary (CIF-based)."""
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if self.state.always_fire:
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return True
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if self.state.never_fire:
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return False
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return True
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def _current_tokens(self) -> mx.array:
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"""Get current token sequence for decoding."""
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toks = self.state.tokens
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if toks[0].shape[0] == 1:
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toks[0] = mx.repeat(toks[0], self.cfg.beam_size, axis=0)
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if not self.state.context.is_empty():
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context_toks = self.state.context.as_mlx_array_beam(self.cfg.beam_size)
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toks = [context_toks] + toks
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# Concatenate all tokens
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if len(toks) > 1:
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current_tokens = mx.concatenate(toks, axis=1)
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else:
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current_tokens = toks[0]
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logger.debug("debug print current_tokens:")
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self.debug_print_tokens(current_tokens)
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return current_tokens
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def debug_print_tokens(self, tokens: mx.array):
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"""Debug print token sequences."""
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tokens_np = np.array(tokens)
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for i in range(min(self.cfg.beam_size, tokens_np.shape[0])):
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logger.debug(self.tokenizer.decode_with_timestamps(tokens_np[i].tolist()))
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def segments_len(self) -> float:
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"""Get total length of audio segments in seconds."""
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return sum(s.shape[0] for s in self.state.segments) / 16000
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def _apply_minseglen(self) -> bool:
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"""Check if we have enough audio to process."""
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segments_len = self.segments_len()
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if segments_len < self.cfg.audio_min_len:
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logger.debug("waiting for next segment")
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return False
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return True
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def insert_audio(self, segment: np.ndarray = None):
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"""Insert audio segment into buffer."""
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if segment is not None:
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if hasattr(segment, 'numpy'):
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segment = segment.numpy()
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self.state.segments.append(segment)
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removed_len = 0
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segments_len = self.segments_len()
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while len(self.state.segments) > 1 and segments_len > self.cfg.audio_max_len:
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removed_len = self.state.segments[0].shape[0] / 16000
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segments_len -= removed_len
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self.state.last_attend_frame -= int(TOKENS_PER_SECOND * removed_len)
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self.state.cumulative_time_offset += removed_len
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self.state.segments = self.state.segments[1:]
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logger.debug(f"remove segments: {len(self.state.segments)} {len(self.state.tokens)}, cumulative offset: {self.state.cumulative_time_offset:.2f}s")
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if len(self.state.tokens) > 1:
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# Convert MLX array to list for context
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token_list = np.array(self.state.tokens[1][0, :]).tolist()
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self.state.context.append_token_ids(token_list)
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self.state.tokens = [self.state.initial_tokens] + self.state.tokens[2:]
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return removed_len
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def _clean_cache(self):
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"""Clean the kv_cache after each inference step."""
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self.state.clean_cache()
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def _suppress_tokens(self, logits: mx.array) -> mx.array:
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"""Apply token suppression to logits."""
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if self.state.suppress_tokens:
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suppress_indices = mx.array(list(self.state.suppress_tokens), dtype=mx.int32)
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logits = logits.at[:, suppress_indices].add(-float('inf'))
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return logits
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def lang_id(self, encoder_features: mx.array) -> Tuple[mx.array, List[dict]]:
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"""Language detection from encoder features."""
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n_audio = encoder_features.shape[0]
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x = mx.array([[self.tokenizer.sot]] * n_audio, dtype=mx.int32)
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logits, _, _ = self.model.decoder(x, encoder_features, kv_cache=None)
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logits = logits[:, 0]
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mask = mx.ones(logits.shape[-1], dtype=mx.bool_)
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language_token_indices = mx.array(list(self.tokenizer.all_language_tokens), dtype=mx.int32)
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mask = mask.at[language_token_indices].add(False)
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logits = mx.where(mask, mx.array(-float('inf')), logits)
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language_tokens = mx.argmax(logits, axis=-1)
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language_token_probs = mx.softmax(logits, axis=-1)
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probs_np = np.array(language_token_probs)
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language_probs = [
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{
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c: float(probs_np[i, j])
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for j, c in zip(self.tokenizer.all_language_tokens, self.tokenizer.all_language_codes)
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}
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for i in range(n_audio)
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]
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self._clean_cache()
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return language_tokens, language_probs
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def infer(self, is_last: bool = False) -> List[ASRToken]:
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"""
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Main inference method.
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Args:
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is_last: Whether this is the final chunk
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Returns:
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List of timestamped ASR tokens
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"""
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new_segment = True
|
|
|
|
if len(self.state.segments) == 0:
|
|
logger.debug("No segments, nothing to do")
|
|
return []
|
|
|
|
if not self._apply_minseglen():
|
|
logger.debug(f"applied minseglen {self.cfg.audio_min_len} > {self.segments_len()}.")
|
|
return []
|
|
|
|
if len(self.state.segments) > 1:
|
|
input_segments = np.concatenate(self.state.segments, axis=0)
|
|
else:
|
|
input_segments = self.state.segments[0]
|
|
|
|
beg_encode = time()
|
|
|
|
mlx_mel_padded = mlx_log_mel_spectrogram(
|
|
audio=input_segments,
|
|
n_mels=self.model.dims.n_mels,
|
|
padding=N_SAMPLES
|
|
)
|
|
mlx_mel = mlx_pad_or_trim(mlx_mel_padded, N_FRAMES, axis=-2)
|
|
encoder_feature = self.model.encoder(mlx_mel[None])
|
|
content_mel_len = int((mlx_mel_padded.shape[0] - mlx_mel.shape[0]) / 2)
|
|
|
|
mx.eval(encoder_feature)
|
|
|
|
end_encode = time()
|
|
logger.debug(f'MLX Encoder duration: {end_encode - beg_encode:.3f}s')
|
|
|
|
if self.cfg.language == "auto" and self.state.detected_language is None and self.state.first_timestamp:
|
|
seconds_since_start = self.segments_len() - self.state.first_timestamp
|
|
if seconds_since_start >= 2.0:
|
|
language_tokens, language_probs = self.lang_id(encoder_feature)
|
|
top_lan, p = max(language_probs[0].items(), key=lambda x: x[1])
|
|
print(f"Detected language: {top_lan} with p={p:.4f}")
|
|
self.create_tokenizer(top_lan)
|
|
self.state.last_attend_frame = -self.cfg.rewind_threshold
|
|
self.state.cumulative_time_offset = 0.0
|
|
self.init_tokens()
|
|
self.init_context()
|
|
self.state.detected_language = top_lan
|
|
logger.info(f"Tokenizer language: {self.tokenizer.language}")
|
|
|
|
self.trim_context()
|
|
current_tokens = self._current_tokens()
|
|
|
|
fire_detected = self.fire_at_boundary(encoder_feature[:, :content_mel_len, :])
|
|
|
|
sum_logprobs = mx.zeros((self.cfg.beam_size,), dtype=mx.float32)
|
|
completed = False
|
|
|
|
attn_of_alignment_heads = None
|
|
most_attended_frame = None
|
|
|
|
token_len_before_decoding = current_tokens.shape[1]
|
|
|
|
l_absolute_timestamps = []
|
|
accumulated_cross_attns = []
|
|
|
|
audio_duration_s = self.segments_len()
|
|
# ~15 text tokens/s is a generous upper bound for speech; TOKENS_PER_SECOND (50)
|
|
# is the mel-frame rate and was causing 10-40x over-allocation on repetition loops.
|
|
max_tokens_per_chunk = max(50, int(audio_duration_s * 15 * 1.5))
|
|
tokens_produced_this_chunk = 0
|
|
|
|
while not completed and current_tokens.shape[1] < self.max_text_len:
|
|
tokens_produced_this_chunk += 1
|
|
|
|
if tokens_produced_this_chunk > max_tokens_per_chunk:
|
|
logger.warning(f"[Loop Detection] Too many tokens ({tokens_produced_this_chunk}) for {audio_duration_s:.2f}s audio. Breaking.")
|
|
current_tokens = current_tokens[:, :token_len_before_decoding]
|
|
break
|
|
|
|
if new_segment:
|
|
tokens_for_logits = current_tokens
|
|
else:
|
|
tokens_for_logits = current_tokens[:, -1:]
|
|
|
|
if self.state.decoder_type == "greedy":
|
|
logits, self.state.kv_cache, cross_qk = self.model.decoder(
|
|
tokens_for_logits, encoder_feature, kv_cache=self.state.kv_cache
|
|
)
|
|
else:
|
|
logits, cross_qk = self.state.inference.logits(tokens_for_logits, encoder_feature)
|
|
|
|
mx.eval(logits)
|
|
|
|
accumulated_cross_attns.append(cross_qk)
|
|
if len(accumulated_cross_attns) > 16:
|
|
accumulated_cross_attns = accumulated_cross_attns[-16:]
|
|
|
|
if new_segment and self.tokenizer.no_speech is not None:
|
|
probs_at_sot = mx.softmax(logits[:, self.state.sot_index, :], axis=-1)
|
|
no_speech_probs = np.array(probs_at_sot[:, self.tokenizer.no_speech]).tolist()
|
|
if no_speech_probs[0] > self.cfg.nonspeech_prob:
|
|
logger.info("no speech, stop")
|
|
break
|
|
|
|
logits = logits[:, -1, :] # Last token logits
|
|
|
|
# Suppress tokens at segment start
|
|
if new_segment:
|
|
blank_tokens = self.tokenizer.encode(" ") + [self.tokenizer.eot]
|
|
logits = logits.at[:, blank_tokens].add(-float('inf'))
|
|
new_segment = False
|
|
|
|
logits = self._suppress_tokens(logits)
|
|
|
|
current_tokens, completed = self.state.token_decoder.update(
|
|
current_tokens, logits, sum_logprobs
|
|
)
|
|
mx.eval(current_tokens)
|
|
|
|
logger.debug(f"Decoding completed: {completed}")
|
|
self.debug_print_tokens(current_tokens)
|
|
|
|
attn_of_alignment_heads = self._process_cross_attention(
|
|
accumulated_cross_attns, content_mel_len
|
|
)
|
|
|
|
most_attended_frames = mx.argmax(attn_of_alignment_heads[:, -1, :], axis=-1)
|
|
most_attended_frames_np = np.array(most_attended_frames)
|
|
|
|
absolute_timestamps = [
|
|
(frame * 0.02 + self.state.cumulative_time_offset)
|
|
for frame in most_attended_frames_np.tolist()
|
|
]
|
|
|
|
logger.debug(str(most_attended_frames_np.tolist()) + " most att frames")
|
|
logger.debug(f"Absolute timestamps: {absolute_timestamps}")
|
|
|
|
most_attended_frame = int(most_attended_frames_np[0])
|
|
l_absolute_timestamps.append(absolute_timestamps[0])
|
|
|
|
if completed:
|
|
current_tokens = current_tokens[:, :-1]
|
|
break
|
|
if not is_last and self.state.last_attend_frame - most_attended_frame > self.cfg.rewind_threshold:
|
|
current_tokens_np = np.array(current_tokens)
|
|
if current_tokens.shape[1] > 1 and current_tokens_np[0, -2] >= DEC_PAD:
|
|
logger.debug("omit rewinding from special tokens")
|
|
self.state.last_attend_frame = most_attended_frame
|
|
else:
|
|
logger.debug(f"[rewind detected] current: {most_attended_frame}, last: {self.state.last_attend_frame}")
|
|
self.state.last_attend_frame = -self.cfg.rewind_threshold
|
|
current_tokens = mx.concatenate(self.state.tokens, axis=1) if len(self.state.tokens) > 0 else self.state.tokens[0]
|
|
break
|
|
else:
|
|
self.state.last_attend_frame = most_attended_frame
|
|
if content_mel_len - most_attended_frame <= (4 if is_last else self.cfg.frame_threshold):
|
|
logger.debug(f"attention reaches the end: {most_attended_frame}/{content_mel_len}")
|
|
current_tokens = current_tokens[:, :-1]
|
|
break
|
|
tokens_to_split = np.array(current_tokens[0, token_len_before_decoding:]).tolist()
|
|
if self.state.pending_incomplete_tokens:
|
|
logger.debug(f"[UTF-8 Fix] Prepending pending tokens: {self.state.pending_incomplete_tokens}")
|
|
tokens_to_split = self.state.pending_incomplete_tokens + tokens_to_split
|
|
|
|
if fire_detected or is_last:
|
|
new_hypothesis = tokens_to_split
|
|
split_words, split_tokens = self.tokenizer.split_to_word_tokens(new_hypothesis)
|
|
else:
|
|
split_words, split_tokens = self.tokenizer.split_to_word_tokens(tokens_to_split)
|
|
if len(split_words) > 1:
|
|
new_hypothesis = [i for sublist in split_tokens[:-1] for i in sublist]
|
|
else:
|
|
new_hypothesis = []
|
|
|
|
logger.debug(f"new_hypothesis: {new_hypothesis}")
|
|
new_tokens = mx.array([new_hypothesis], dtype=mx.int32)
|
|
new_tokens = mx.repeat(new_tokens, self.cfg.beam_size, axis=0)
|
|
self.state.tokens.append(new_tokens)
|
|
|
|
logger.info(f"Output: {self.tokenizer.decode(new_hypothesis)}")
|
|
|
|
self._clean_cache()
|
|
|
|
if len(l_absolute_timestamps) >= 2 and self.state.first_timestamp is None:
|
|
self.state.first_timestamp = l_absolute_timestamps[0]
|
|
timestamped_words = []
|
|
timestamp_idx = 0
|
|
replacement_char = "\ufffd"
|
|
|
|
for word, word_tokens in zip(split_words, split_tokens):
|
|
if replacement_char in word:
|
|
logger.warning(f"[UTF-8 Filter] Skipping: {repr(word)}")
|
|
timestamp_idx += len(word_tokens)
|
|
continue
|
|
|
|
try:
|
|
current_timestamp = l_absolute_timestamps[timestamp_idx]
|
|
except IndexError:
|
|
pass
|
|
timestamp_idx += len(word_tokens)
|
|
|
|
timestamp_entry = ASRToken(
|
|
start=round(current_timestamp, 2),
|
|
end=round(current_timestamp + 0.1, 2),
|
|
text=word,
|
|
speaker=self.state.speaker,
|
|
detected_language=self.state.detected_language
|
|
).with_offset(self.state.global_time_offset)
|
|
timestamped_words.append(timestamp_entry)
|
|
self.state.pending_incomplete_tokens = []
|
|
MAX_PENDING_TOKENS = 10
|
|
if split_words and replacement_char in split_words[-1]:
|
|
if len(split_tokens[-1]) <= MAX_PENDING_TOKENS:
|
|
self.state.pending_incomplete_tokens = split_tokens[-1]
|
|
logger.debug(f"[UTF-8 Fix] Holding incomplete tokens")
|
|
else:
|
|
logger.warning(f"[UTF-8 Fix] Skipping too many tokens")
|
|
|
|
return timestamped_words
|
|
|
|
def _process_cross_attention(
|
|
self,
|
|
cross_attns: List[List[mx.array]],
|
|
content_mel_len: int
|
|
) -> mx.array:
|
|
"""
|
|
Process cross-attention weights for alignment.
|
|
|
|
Args:
|
|
cross_attns: List of cross-attention from each forward pass
|
|
Each element is a list of mx.arrays per layer
|
|
content_mel_len: Length of actual audio content
|
|
|
|
Returns:
|
|
Processed attention tensor, shape (batch, seq_len, content_mel_len)
|
|
"""
|
|
attn_of_alignment_heads = [[] for _ in range(self.state.num_align_heads)]
|
|
num_decoder_layers = self.num_decoder_layers
|
|
|
|
if cross_attns and isinstance(cross_attns[0], list):
|
|
flattened_attns = [attn for layer_list in cross_attns for attn in layer_list]
|
|
else:
|
|
flattened_attns = cross_attns
|
|
|
|
for idx, attn_mat in enumerate(flattened_attns):
|
|
if attn_mat is None:
|
|
continue
|
|
|
|
layer_rank = idx % num_decoder_layers
|
|
align_heads_in_layer = self.state.align_source.get(layer_rank, [])
|
|
|
|
if len(align_heads_in_layer) == 0:
|
|
continue
|
|
attn_mat = mx.softmax(attn_mat, axis=-1)
|
|
|
|
for align_head_rank, head_id in align_heads_in_layer:
|
|
if self.cfg.beam_size == 1:
|
|
if attn_mat.ndim == 4:
|
|
a = attn_mat[0, head_id, :, :]
|
|
else:
|
|
a = attn_mat[head_id, :, :]
|
|
a = a[None, :, :]
|
|
else:
|
|
a = attn_mat[:, head_id, :, :]
|
|
attn_of_alignment_heads[align_head_rank].append(a)
|
|
tmp = []
|
|
for mat in attn_of_alignment_heads:
|
|
if mat:
|
|
t = mx.concatenate(mat, axis=1)
|
|
tmp.append(t)
|
|
|
|
if not tmp:
|
|
return mx.zeros((self.cfg.beam_size, 1, content_mel_len))
|
|
attn_of_alignment_heads = mx.stack(tmp, axis=1)
|
|
|
|
std = mx.std(attn_of_alignment_heads, axis=-2, keepdims=True)
|
|
mean = mx.mean(attn_of_alignment_heads, axis=-2, keepdims=True)
|
|
attn_of_alignment_heads = (attn_of_alignment_heads - mean) / (std + 1e-8)
|
|
|
|
attn_of_alignment_heads = mlx_median_filter(attn_of_alignment_heads, 7)
|
|
|
|
attn_of_alignment_heads = mx.mean(attn_of_alignment_heads, axis=1)
|
|
|
|
attn_of_alignment_heads = attn_of_alignment_heads[:, :, :content_mel_len]
|
|
|
|
mx.eval(attn_of_alignment_heads)
|
|
return attn_of_alignment_heads
|
|
|