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| import torch | |
| #from transformers import pipeline | |
| #from transformers.pipelines.audio_utils import ffmpeg_read | |
| from speechscore import SpeechScore | |
| import gradio as gr | |
| MODEL_NAME = "alibabasglab/speechscore" | |
| BATCH_SIZE = 1 | |
| device = 0 if torch.cuda.is_available() else "cpu" | |
| """ | |
| mySpeechScore = SpeechScore([ | |
| 'PESQ','DNSMOS' | |
| ]) | |
| """ | |
| # Copied from https://github.com/openai/whisper/blob/c09a7ae299c4c34c5839a76380ae407e7d785914/whisper/utils.py#L50 | |
| def format_timestamp(seconds: float, always_include_hours: bool = False, decimal_marker: str = "."): | |
| if seconds is not None: | |
| milliseconds = round(seconds * 1000.0) | |
| hours = milliseconds // 3_600_000 | |
| milliseconds -= hours * 3_600_000 | |
| minutes = milliseconds // 60_000 | |
| milliseconds -= minutes * 60_000 | |
| seconds = milliseconds // 1_000 | |
| milliseconds -= seconds * 1_000 | |
| hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" | |
| return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" | |
| else: | |
| # we have a malformed timestamp so just return it as is | |
| return seconds | |
| def score(test_file, ref_file, score_list, return_timestamps): | |
| mySpeechScore = SpeechScore(score_list) | |
| scores = mySpeechScore(test_path=test_file, reference_path=ref_file, window=None, score_rate=16000, return_mean=False) | |
| return scores | |
| demo = gr.Blocks() | |
| file_score = gr.Interface( | |
| fn=score, | |
| inputs=[ | |
| gr.Audio(sources=["upload"], label="test file", type="filepath"), | |
| gr.Audio(sources=["upload"], label="reference file", type="filepath"), | |
| #gr.Radio(["without reference", "with reference"], label="Task", info="choose non-instrusive or instrusive scoring"), | |
| #gr.Checkbox(default=False, label="DNSMOS"), | |
| #gr.Checkbox(default=False, label="PESQ"), | |
| gr.Dropdown( | |
| ["DNSMOS", "PESQ", "NB-PESQ", "SISNR"], value=["DNSMOS", "PESQ"], multiselect=True, label="Scores", info="By checking the following scores, include them in the output." | |
| ), | |
| ], | |
| outputs="text", | |
| #layout="horizontal", | |
| #theme="huggingface", | |
| title="Score speech from a file", | |
| description=( | |
| "Score audio inputs with the click of a button! Demo uses the" | |
| " commonly used speech quality assessment methods for the audio files" | |
| " of arbitrary length." | |
| ), | |
| ) | |
| with demo: | |
| gr.TabbedInterface([file_score], ["Score Audio File"]) | |
| demo.launch() |