Create app.py

app.py

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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([
+        'SRMR'
+    ])
+
+
+# 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(file, task, return_timestamps):
+    scores = mySpeechScore(test_path=file, reference_path=None, window=None, score_rate=16000, return_mean=True)
+    return scores
+
+
+demo = gr.Blocks()
+
+mic_score = gr.Interface(
+    fn=score,
+    inputs=[
+        gr.Audio(sources=["microphone"],
+                waveform_options=gr.WaveformOptions(
+                waveform_color="#01C6FF",
+                waveform_progress_color="#0066B4",
+                skip_length=2,
+                show_controls=False,
+                ),
+            ),
+        gr.Radio(["absolute_score", "relative_score"], label="Task", default="absolute_score"),
+        gr.Checkbox(default=False, label="Return timestamps"),
+    ],
+    outputs="text",
+    layout="horizontal",
+    theme="huggingface",
+    title="Score speech from microphone",
+    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."
+    ),
+    allow_flagging="never",
+)
+
+file_score = gr.Interface(
+    fn=score,
+    inputs=[
+        gr.Audio(sources=["upload"], optional=True, label="Audio file", type="filepath"),
+        gr.Radio(["absolute_score", "relative_score"], label="Task", default="absolute_score"),
+        gr.Checkbox(default=False, label="Return timestamps"),
+    ],
+    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."
+    ),
+    examples=[
+        ["./example.flac", "score", False],
+        ["./example.flac", "score", True],
+    ],
+    cache_examples=True,
+    allow_flagging="never",
+)
+
+with demo:
+    gr.TabbedInterface([mic_score, file_score], ["Score Microphone", "Score Audio File"])
+
+demo.launch(enable_queue=True)