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If you use more than 1 number of beams make tempature at least 1.0

```
https://github.com/coqui-ai/TTS/blob/04901fb2e4b74953bb5733205adcb7cb13655a06/TTS/tts/models/xtts.py#L473
Args:
            text: (str) Text to be spoken.

            ref_audio_path: (str) Path to a reference audio file to be used for cloning. This audio file should be >3
                seconds long.

            language: (str) Language of the voice to be generated.

            temperature: (float) The softmax temperature of the autoregressive model. Defaults to 0.65.

            length_penalty: (float) A length penalty applied to the autoregressive decoder. Higher settings causes the
                model to produce more terse outputs. Defaults to 1.0.

            repetition_penalty: (float) A penalty that prevents the autoregressive decoder from repeating itself during
                decoding. Can be used to reduce the incidence of long silences or "uhhhhhhs", etc. Defaults to 2.0.

            top_k: (int) K value used in top-k sampling. [0,inf]. Lower values mean the decoder produces more "likely"
                (aka boring) outputs. Defaults to 50.

            top_p: (float) P value used in nucleus sampling. (0,1]. Lower values mean the decoder produces more "likely"
                (aka boring) outputs. Defaults to 0.8.

            gpt_cond_len: (int) Length of the audio used for cloning. If audio is shorter, then audio length is used
                else the first `gpt_cond_len` secs is used. Defaults to 30 seconds.

            gpt_cond_chunk_len: (int) Chunk length used for cloning. It must be <= `gpt_cond_len`.
                If gpt_cond_len == gpt_cond_chunk_len, no chunking. Defaults to 6 seconds.
```
