added transformers based demo for sst

demo/streaming_wav2vec/requirements.txt

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+deepspeech==0.8.2

demo/streaming_wav2vec/run.py

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+from deepspeech import Model
+import gradio as gr
+import scipy.io.wavfile
+import numpy as np
+
+model_file_path = "deepspeech-0.8.2-models.pbmm"
+lm_file_path = "deepspeech-0.8.2-models.scorer"
+beam_width = 100
+lm_alpha = 0.93
+lm_beta = 1.18
+
+model = Model(model_file_path)
+model.enableExternalScorer(lm_file_path)
+model.setScorerAlphaBeta(lm_alpha, lm_beta)
+model.setBeamWidth(beam_width)
+
+
+def reformat_freq(sr, y):
+    if sr not in (
+        48000,
+        16000,
+    ):  # Deepspeech only supports 16k, (we convert 48k -> 16k)
+        raise ValueError("Unsupported rate", sr)
+    if sr == 48000:
+        y = (
+            ((y / max(np.max(y), 1)) * 32767)
+            .reshape((-1, 3))
+            .mean(axis=1)
+            .astype("int16")
+        )
+        sr = 16000
+    return sr, y
+
+
+def transcribe(speech, stream):
+    _, y = reformat_freq(*speech)
+    if stream is None:
+        stream = model.createStream()
+    stream.feedAudioContent(y)
+    text = stream.intermediateDecode()
+    return text, stream
+
+gr.Interface(transcribe, ["microphone", "state"], ["text", "state"], live=True).launch()