Add translation backend

Translate from any language to any language with alirezamsh/small100
running in a thread and reading from a queue shared with transcription thread.

Signed-off-by: makaveli10 <vineet.suryan@collabora.com>

whisper_live/backend/base.py

@@ -2,6 +2,7 @@ import json
 import logging
 import threading
 import time
+import queue
 import numpy as np
 
 
@@ -31,6 +32,7 @@ class ServeClientBase(object):
         no_speech_thresh=0.45,
         clip_audio=False,
         same_output_threshold=10,
+        translation_queue=None,
     ):
         self.client_uid = client_uid
         self.websocket = websocket
@@ -50,6 +52,7 @@ class ServeClientBase(object):
         self.same_output_count = 0
         self.transcript = []
         self.end_time_for_same_output = None
+        self.translation_queue = translation_queue
 
         # threading
         self.lock = threading.Lock()
@@ -307,7 +310,14 @@ class ServeClientBase(object):
                     continue
                 if self.get_segment_no_speech_prob(s) > self.no_speech_thresh:
                     continue
-                self.transcript.append(self.format_segment(start, end, text_, completed=True))
+                completed_segment = self.format_segment(start, end, text_, completed=True)
+                self.transcript.append(completed_segment)
+
+                if self.translation_queue:
+                    try:
+                        self.translation_queue.put(completed_segment.copy(), timeout=0.1)
+                    except queue.Full:
+                        logging.warning("Translation queue is full, skipping segment")
                 offset = min(duration, self.get_segment_end(s))
 
         # Process the last segment if its no_speech_prob is acceptable.
@@ -340,12 +350,20 @@ class ServeClientBase(object):
             if not self.text or self.text[-1].strip().lower() != self.current_out.strip().lower():
                 self.text.append(self.current_out)
                 with self.lock:
-                    self.transcript.append(self.format_segment(
+                    completed_segment = self.format_segment(
                         self.timestamp_offset,
                         self.timestamp_offset + min(duration, self.end_time_for_same_output),
                         self.current_out,
                         completed=True
-                    ))
+                    )
+                    self.transcript.append(completed_segment)
+
+                    if self.translation_queue:
+                        try:
+                            self.translation_queue.put(completed_segment.copy(), timeout=0.1)
+                        except queue.Full:
+                            logging.warning("Translation queue is full, skipping segment")
+
             self.current_out = ''
             offset = min(duration, self.end_time_for_same_output)
             self.same_output_count = 0

whisper_live/backend/faster_whisper_backend.py

@@ -30,8 +30,9 @@ class ServeClientFasterWhisper(ServeClientBase):
         send_last_n_segments=10,
         no_speech_thresh=0.45,
         clip_audio=False,
-        same_output_threshold=10,
-        cache_path="~/.cache/whisper-live/"
+        same_output_threshold=7,
+        cache_path="~/.cache/whisper-live/",
+        translation_queue=None,
     ):
         """
         Initialize a ServeClient instance.
@@ -61,6 +62,7 @@ class ServeClientFasterWhisper(ServeClientBase):
             no_speech_thresh,
             clip_audio,
             same_output_threshold,
+            translation_queue
         )
         self.cache_path = cache_path
         self.model_sizes = [

whisper_live/backend/tokenization_small100.py

@@ -0,0 +1,365 @@
+# Copyright (c) 2022 Idiap Research Institute, http://www.idiap.ch/
+# Written by Alireza Mohammadshahi <alireza.mohammadshahi@idiap.ch>
+# This is a modified version of https://github.com/huggingface/transformers/blob/main/src/transformers/models/m2m_100/tokenization_m2m_100.py 
+# which owns by Fariseq Authors and The HuggingFace Inc. team.
+#
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for SMALL100."""
+import json
+import os
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import sentencepiece
+
+from transformers.tokenization_utils import BatchEncoding, PreTrainedTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+SPIECE_UNDERLINE = "▁"
+
+VOCAB_FILES_NAMES = {
+    "vocab_file": "vocab.json",
+    "spm_file": "sentencepiece.bpe.model",
+    "tokenizer_config_file": "tokenizer_config.json",
+}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "alirezamsh/small100": "https://huggingface.co/alirezamsh/small100/resolve/main/vocab.json",
+    },
+    "spm_file": {
+        "alirezamsh/small100": "https://huggingface.co/alirezamsh/small100/resolve/main/sentencepiece.bpe.model",
+    },
+    "tokenizer_config_file": {
+        "alirezamsh/small100": "https://huggingface.co/alirezamsh/small100/resolve/main/tokenizer_config.json",
+    },
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "alirezamsh/small100": 1024,
+}
+
+# fmt: off
+FAIRSEQ_LANGUAGE_CODES = {
+    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"]
+}
+# fmt: on
+
+
+class SMALL100Tokenizer(PreTrainedTokenizer):
+    """
+    Construct an SMALL100 tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+    Args:
+        vocab_file (`str`):
+            Path to the vocabulary file.
+        spm_file (`str`):
+            Path to [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
+            contains the vocabulary.
+        tgt_lang (`str`, *optional*):
+            A string representing the target language.
+        eos_token (`str`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+        sep_token (`str`, *optional*, defaults to `"</s>"`):
+            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
+            sequence classification or for a text and a question for question answering. It is also used as the last
+            token of a sequence built with special tokens.
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+        pad_token (`str`, *optional*, defaults to `"<pad>"`):
+            The token used for padding, for example when batching sequences of different lengths.
+        language_codes (`str`, *optional*):
+            What language codes to use. Should be `"m2m100"`.
+        sp_model_kwargs (`dict`, *optional*):
+            Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
+            SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
+            to set:
+            - `enable_sampling`: Enable subword regularization.
+            - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
+              - `nbest_size = {0,1}`: No sampling is performed.
+              - `nbest_size > 1`: samples from the nbest_size results.
+              - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
+                using forward-filtering-and-backward-sampling algorithm.
+            - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
+              BPE-dropout.
+    Examples:
+    ```python
+    >>> from tokenization_small100 import SMALL100Tokenizer
+    >>> tokenizer = SMALL100Tokenizer.from_pretrained("alirezamsh/small100", tgt_lang="ro")
+    >>> src_text = " UN Chief Says There Is No Military Solution in Syria"
+    >>> tgt_text = "Şeful ONU declară că nu există o soluţie militară în Siria"
+    >>> model_inputs = tokenizer(src_text, text_target=tgt_text, return_tensors="pt")
+    >>> model(**model_inputs)  # should work
+    ```"""
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    model_input_names = ["input_ids", "attention_mask"]
+
+    prefix_tokens: List[int] = []
+    suffix_tokens: List[int] = []
+
+    def __init__(
+        self,
+        vocab_file,
+        spm_file,
+        tgt_lang=None,
+        bos_token="<s>",
+        eos_token="</s>",
+        sep_token="</s>",
+        pad_token="<pad>",
+        unk_token="<unk>",
+        language_codes="m2m100",
+        sp_model_kwargs: Optional[Dict[str, Any]] = None,
+        num_madeup_words=8,
+        **kwargs,
+    ) -> None:
+        self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+
+        self.language_codes = language_codes
+        fairseq_language_code = FAIRSEQ_LANGUAGE_CODES[language_codes]
+        self.lang_code_to_token = {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}
+
+        kwargs["additional_special_tokens"] = kwargs.get("additional_special_tokens", [])
+        kwargs["additional_special_tokens"] += [
+            self.get_lang_token(lang_code)
+            for lang_code in fairseq_language_code
+            if self.get_lang_token(lang_code) not in kwargs["additional_special_tokens"]
+        ]
+
+        self.vocab_file = vocab_file
+        self.encoder = load_json(vocab_file)
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.spm_file = spm_file
+        self.sp_model = load_spm(spm_file, self.sp_model_kwargs)
+
+        self.encoder_size = len(self.encoder)
+
+        self.lang_token_to_id = {
+            self.get_lang_token(lang_code): self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)
+        }
+        self.lang_code_to_id = {lang_code: self.encoder_size + i for i, lang_code in enumerate(fairseq_language_code)}
+        self.id_to_lang_token = {v: k for k, v in self.lang_token_to_id.items()}
+
+        self._tgt_lang = tgt_lang if tgt_lang is not None else "en"
+        self.cur_lang_id = self.get_lang_id(self._tgt_lang)
+        self.num_madeup_words = num_madeup_words
+        
+        super().__init__(
+            tgt_lang=tgt_lang,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            sep_token=sep_token,
+            unk_token=unk_token,
+            pad_token=pad_token,
+            language_codes=language_codes,
+            sp_model_kwargs=self.sp_model_kwargs,
+            num_madeup_words=num_madeup_words,
+            **kwargs,
+        )
+        
+        self.set_lang_special_tokens(self._tgt_lang)
+
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self.encoder) + len(self.lang_token_to_id) + self.num_madeup_words
+
+    @property
+    def tgt_lang(self) -> str:
+        return self._tgt_lang
+
+    @tgt_lang.setter
+    def tgt_lang(self, new_tgt_lang: str) -> None:
+        self._tgt_lang = new_tgt_lang
+        self.set_lang_special_tokens(self._tgt_lang)
+
+    def _tokenize(self, text: str) -> List[str]:
+        return self.sp_model.encode(text, out_type=str)
+
+    def _convert_token_to_id(self, token):
+        if token in self.lang_token_to_id:
+            return self.lang_token_to_id[token]
+        return self.encoder.get(token, self.encoder[self.unk_token])
+
+    def _convert_id_to_token(self, index: int) -> str:
+        """Converts an index (integer) in a token (str) using the decoder."""
+        if index in self.id_to_lang_token:
+            return self.id_to_lang_token[index]
+        return self.decoder.get(index, self.unk_token)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        """Converts a sequence of tokens (strings for sub-words) in a single string."""
+        return self.sp_model.decode(tokens)
+
+    def get_special_tokens_mask(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+    ) -> List[int]:
+        """
+        Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+        special tokens using the tokenizer `prepare_for_model` method.
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+                Whether or not the token list is already formatted with special tokens for the model.
+        Returns:
+            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+        """
+
+        if already_has_special_tokens:
+            return super().get_special_tokens_mask(
+                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+            )
+
+        prefix_ones = [1] * len(self.prefix_tokens)
+        suffix_ones = [1] * len(self.suffix_tokens)
+        if token_ids_1 is None:
+            return prefix_ones + ([0] * len(token_ids_0)) + suffix_ones
+        return prefix_ones + ([0] * len(token_ids_0)) + ([0] * len(token_ids_1)) + suffix_ones
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        """
+        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
+        adding special tokens. An MBART sequence has the following format, where `X` represents the sequence:
+        - `input_ids` (for encoder) `X [eos, src_lang_code]`
+        - `decoder_input_ids`: (for decoder) `X [eos, tgt_lang_code]`
+        BOS is never used. Pairs of sequences are not the expected use case, but they will be handled without a
+        separator.
+        Args:
+            token_ids_0 (`List[int]`):
+                List of IDs to which the special tokens will be added.
+            token_ids_1 (`List[int]`, *optional*):
+                Optional second list of IDs for sequence pairs.
+        Returns:
+            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
+        """
+        if token_ids_1 is None:
+            if self.prefix_tokens is None:
+                return token_ids_0 + self.suffix_tokens
+            else:
+                return self.prefix_tokens + token_ids_0 + self.suffix_tokens
+        # We don't expect to process pairs, but leave the pair logic for API consistency
+        if self.prefix_tokens is None:
+            return token_ids_0 + token_ids_1 + self.suffix_tokens
+        else:
+            return self.prefix_tokens + token_ids_0 + token_ids_1 + self.suffix_tokens
+
+    def get_vocab(self) -> Dict:
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def __getstate__(self) -> Dict:
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    def __setstate__(self, d: Dict) -> None:
+        self.__dict__ = d
+
+        # for backward compatibility
+        if not hasattr(self, "sp_model_kwargs"):
+            self.sp_model_kwargs = {}
+
+        self.sp_model = load_spm(self.spm_file, self.sp_model_kwargs)
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        save_dir = Path(save_directory)
+        if not save_dir.is_dir():
+            raise OSError(f"{save_directory} should be a directory")
+        vocab_save_path = save_dir / (
+            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
+        )
+        spm_save_path = save_dir / (
+            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
+        )
+
+        save_json(self.encoder, vocab_save_path)
+
+        if os.path.abspath(self.spm_file) != os.path.abspath(spm_save_path) and os.path.isfile(self.spm_file):
+            copyfile(self.spm_file, spm_save_path)
+        elif not os.path.isfile(self.spm_file):
+            with open(spm_save_path, "wb") as fi:
+                content_spiece_model = self.sp_model.serialized_model_proto()
+                fi.write(content_spiece_model)
+
+        return (str(vocab_save_path), str(spm_save_path))
+
+    def prepare_seq2seq_batch(
+        self,
+        src_texts: List[str],
+        tgt_texts: Optional[List[str]] = None,
+        tgt_lang: str = "ro",
+        **kwargs,
+    ) -> BatchEncoding:
+        self.tgt_lang = tgt_lang
+        self.set_lang_special_tokens(self.tgt_lang)
+        return super().prepare_seq2seq_batch(src_texts, tgt_texts, **kwargs)
+
+    def _build_translation_inputs(self, raw_inputs, tgt_lang: Optional[str], **extra_kwargs):
+        """Used by translation pipeline, to prepare inputs for the generate function"""
+        if tgt_lang is None:
+            raise ValueError("Translation requires a `tgt_lang` for this model")
+        self.tgt_lang = tgt_lang
+        inputs = self(raw_inputs, add_special_tokens=True, **extra_kwargs)
+        return inputs
+
+    def _switch_to_input_mode(self):
+        self.set_lang_special_tokens(self.tgt_lang)
+
+    def _switch_to_target_mode(self):
+        self.prefix_tokens = None
+        self.suffix_tokens = [self.eos_token_id]        
+
+    def set_lang_special_tokens(self, src_lang: str) -> None:
+        """Reset the special tokens to the tgt lang setting. No prefix and suffix=[eos, tgt_lang_code]."""
+        lang_token = self.get_lang_token(src_lang)
+        self.cur_lang_id = self.lang_token_to_id[lang_token]
+        self.prefix_tokens = [self.cur_lang_id]
+        self.suffix_tokens = [self.eos_token_id]
+
+    def get_lang_token(self, lang: str) -> str:
+        return self.lang_code_to_token[lang]
+
+    def get_lang_id(self, lang: str) -> int:
+        lang_token = self.get_lang_token(lang)
+        return self.lang_token_to_id[lang_token]
+
+
+def load_spm(path: str, sp_model_kwargs: Dict[str, Any]) -> sentencepiece.SentencePieceProcessor:
+    spm = sentencepiece.SentencePieceProcessor(**sp_model_kwargs)
+    spm.Load(str(path))
+    return spm
+
+
+def load_json(path: str) -> Union[Dict, List]:
+    with open(path, "r") as f:
+        return json.load(f)
+
+
+def save_json(data, path: str) -> None:
+    with open(path, "w") as f:
+        json.dump(data, f, indent=2)

whisper_live/backend/translation_backend.py

@@ -0,0 +1,218 @@
+import json
+import logging
+import threading
+import time
+import queue
+from typing import Dict, Any, Optional
+import torch
+import threading
+from transformers import M2M100ForConditionalGeneration
+from whisper_live.backend.tokenization_small100 import SMALL100Tokenizer
+
+from whisper_live.backend.base import ServeClientBase
+
+
+class ServeClientTranslation(ServeClientBase):
+    """
+    Handles translation of completed transcription segments in a separate thread.
+    Reads from a queue populated by the transcription backend and sends translated
+    segments back to the client via WebSocket.
+    """
+    
+    def __init__(
+        self,
+        client_uid,
+        websocket,
+        translation_queue,
+        target_language="fr", 
+        send_last_n_segments=10,
+        model_name="alirezamsh/small100"
+    ):
+        """
+        Initialize the translation client.
+        
+        Args:
+            client_uid (str): Unique identifier for the client
+            websocket: WebSocket connection to the client
+            translation_queue (queue.Queue): Queue containing completed segments to translate
+            target_language (str): Target language code (default: "fr" for French)
+            send_last_n_segments (int): Number of recent translated segments to send
+            model_name (str): Translation model name to use
+        """
+        super().__init__(client_uid, websocket, send_last_n_segments)
+        self.translation_queue = translation_queue
+        self.target_language = target_language
+        self.model_name = model_name
+        self.translated_segments = []
+        self.translation_model = None
+        self.tokenizer = None
+        self.device = None
+        self.model_loaded = False
+        self.load_translation_model()
+        
+    def load_translation_model(self):
+        """Load the translation model and tokenizer."""
+        try:
+            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+            logging.info(f"Loading translation model on device: {self.device}")
+            
+            self.translation_model = M2M100ForConditionalGeneration.from_pretrained(
+                self.model_name
+            ).to(self.device)
+            self.tokenizer = SMALL100Tokenizer.from_pretrained(self.model_name)
+            self.tokenizer.tgt_lang = self.target_language
+            
+            self.model_loaded = True
+            logging.info(f"Translation model loaded successfully. Target language: {self.target_language}")
+        except Exception as e:
+            logging.error(f"Failed to load translation model: {e}")
+            self.translation_model = None
+            self.tokenizer = None
+            self.model_loaded = False
+    
+    def translate_text(self, text: str) -> str:
+        """
+        Translate a single text segment.
+        
+        Args:
+            text (str): Text to translate
+            
+        Returns:
+            str: Translated text or original text if translation fails
+        """
+        if not self.model_loaded or not text.strip():
+            return text
+            
+        try:
+            # Encode input and move to device
+            encoded_input = self.tokenizer(text, return_tensors="pt").to(self.device)
+            
+            # Generate translation
+            with torch.no_grad():
+                generated_tokens = self.translation_model.generate(**encoded_input)
+            
+            # Decode output
+            output = self.tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
+            return output[0] if output else text
+            
+        except Exception as e:
+            logging.error(f"Translation failed for text '{text}': {e}")
+            return text
+    
+    def process_translation_queue(self):
+        """
+        Process segments from the translation queue.
+        Continuously reads from the queue until None is received (exit signal).
+        """
+        logging.info(f"Starting translation processing for client {self.client_uid}")
+        
+        while not self.exit:
+            try:
+                # Get segment from queue with timeout
+                segment = self.translation_queue.get(timeout=1.0)
+                
+                # Check for exit signal
+                if segment is None:
+                    logging.info(f"Received exit signal for translation client {self.client_uid}")
+                    break
+                    
+                # Only translate completed segments
+                if not segment.get("completed", False):
+                    self.translation_queue.task_done()
+                    continue
+                    
+                # Translate the segment
+                original_text = segment.get("text", "")
+                translated_text = self.translate_text(original_text)
+                
+                # Create translated segment
+                translated_segment = {
+                    "start": segment["start"],
+                    "end": segment["end"],
+                    "text": translated_text,
+                    "completed": segment.get("completed", False),
+                    "target_language": self.target_language
+                }
+                
+                self.translated_segments.append(translated_segment)
+                segments_to_send = self.prepare_translated_segments()
+                self.send_translation_to_client(segments_to_send)
+                
+                self.translation_queue.task_done()
+                
+            except queue.Empty:
+                continue
+            except Exception as e:
+                logging.error(f"Error processing translation queue: {e}")
+                continue
+        
+        logging.info(f"Translation processing ended for client {self.client_uid}")
+    
+    def prepare_translated_segments(self):
+        """
+        Prepare the last n translated segments to send to client.
+        
+        Returns:
+            list: List of recent translated segments
+        """
+        if len(self.translated_segments) >= self.send_last_n_segments:
+            return self.translated_segments[-self.send_last_n_segments:]
+        return self.translated_segments[:]
+    
+    def send_translation_to_client(self, translated_segments):
+        """
+        Send translated segments to the client via WebSocket.
+        
+        Args:
+            translated_segments (list): List of translated segments to send
+        """
+        try:
+            self.websocket.send(
+                json.dumps({
+                    "uid": self.client_uid,
+                    "translated_segments": translated_segments,
+                })
+            )
+        except Exception as e:
+            logging.error(f"[ERROR]: Sending translation data to client: {e}")
+    
+    def speech_to_text(self):
+        """
+        Override parent method to handle translation processing.
+        This method will be called when the translation thread starts.
+        """
+        self.process_translation_queue()
+    
+    def set_target_language(self, language: str):
+        """
+        Change the target language for translation.
+        
+        Args:
+            language (str): New target language code
+        """
+        self.target_language = language
+        if self.tokenizer:
+            self.tokenizer.tgt_lang = language
+            logging.info(f"Target language changed to: {language}")
+    
+    def cleanup(self):
+        """Clean up translation resources."""
+        logging.info(f"Cleaning up translation resources for client {self.client_uid}")
+        self.exit = True
+        
+        try:
+            self.translation_queue.put(None, timeout=1.0)
+        except:
+            pass
+        
+        self.translated_segments.clear()
+        
+        if self.translation_model:
+            del self.translation_model
+            self.translation_model = None
+        if self.tokenizer:
+            del self.tokenizer
+            self.tokenizer = None
+        
+        if self.device and self.device.type == 'cuda':
+            torch.cuda.empty_cache()