Repository: coqui-ai/TTS
File: TTS/tts/models/xtts.py (Python)

Code before:
        return gpt_cond_latents, speaker_embedding

    def synthesize(self, text, config, speaker_wav, language, **kwargs):
        """Synthesize speech with the given input text.

        Args:
            text (str): Input text.
            config (XttsConfig): Config with inference parameters.
            speaker_wav (list): List of paths to the speaker audio files to be used for cloning.
            language (str): Language ID of the speaker.
            **kwargs: Inference settings. See `inference()`.

        Returns:
            A dictionary of the output values with `wav` as output waveform, `deterministic_seed` as seed used at inference,
            `text_input` as text token IDs after tokenizer, `voice_samples` as samples used for cloning, `conditioning_latents`
            as latents used at inference.

        """
        return self.inference_with_config(text, config, ref_audio_path=speaker_wav, language=language, **kwargs)

    def inference_with_config(self, text, config, ref_audio_path, language, **kwargs):
        """
        inference with config
        """
        assert (
            language in self.config.languages
        ), f" ❗ Language {language} is not supported. Supported languages are {self.config.languages}"
        # Use generally found best tuning knobs for generation.
        settings = {
            "temperature": config.temperature,
            "length_penalty": config.length_penalty,
            "repetition_penalty": config.repetition_penalty,
            "top_k": config.top_k,
            "top_p": config.top_p,
            "gpt_cond_len": config.gpt_cond_len,
            "gpt_cond_chunk_len": config.gpt_cond_chunk_len,
            "max_ref_len": config.max_ref_len,
            "sound_norm_refs": config.sound_norm_refs,
        }
        settings.update(kwargs)  # allow overriding of preset settings with kwargs
        return self.full_inference(text, ref_audio_path, language, **settings)

    @torch.inference_mode()
    def full_inference(
        self,
        text,
        ref_audio_path,
        language,
        # GPT inference
        temperature=0.75,
        length_penalty=1.0,

Review comment (suggestion):
It will return "zh-cn"  when language == "zh" and it will check if not. It is ok but for both conditional returns the same data type it should be  True if language == "zh" else language in self.config.languages.

Code after:
        return gpt_cond_latents, speaker_embedding

    def synthesize(self, text, config, speaker_wav, language, **kwargs):
        """Synthesize speech with the given input text.

        Args:
            text (str): Input text.
            config (XttsConfig): Config with inference parameters.
            speaker_wav (list): List of paths to the speaker audio files to be used for cloning.
            language (str): Language ID of the speaker.
            **kwargs: Inference settings. See `inference()`.

        Returns:
            A dictionary of the output values with `wav` as output waveform, `deterministic_seed` as seed used at inference,
            `text_input` as text token IDs after tokenizer, `voice_samples` as samples used for cloning, `conditioning_latents`
            as latents used at inference.

        """
        return self.inference_with_config(text, config, ref_audio_path=speaker_wav, language=language, **kwargs)

    def inference_with_config(self, text, config, ref_audio_path, language, **kwargs):
        """
        inference with config
        """
        assert (
            "zh-cn" if language == "zh" else language in self.config.languages
        ), f" ❗ Language {language} is not supported. Supported languages are {self.config.languages}"
        # Use generally found best tuning knobs for generation.
        settings = {
            "temperature": config.temperature,
            "length_penalty": config.length_penalty,
            "repetition_penalty": config.repetition_penalty,
            "top_k": config.top_k,
            "top_p": config.top_p,
            "gpt_cond_len": config.gpt_cond_len,
            "gpt_cond_chunk_len": config.gpt_cond_chunk_len,
            "max_ref_len": config.max_ref_len,
            "sound_norm_refs": config.sound_norm_refs,
        }
        settings.update(kwargs)  # allow overriding of preset settings with kwargs
        return self.full_inference(text, ref_audio_path, language, **settings)

    @torch.inference_mode()
    def full_inference(
        self,
        text,
        ref_audio_path,
        language,
        # GPT inference
        temperature=0.75,
        length_penalty=1.0,