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README.md

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+---
+title: Dhivehi Mms Zeroshot
+emoji: 📈
+colorFrom: yellow
+colorTo: red
+sdk: gradio
+sdk_version: 4.42.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import spaces
+import gradio as gr
+import librosa
+import torch
+
+from transformers import Wav2Vec2ForCTC, AutoProcessor
+from huggingface_hub import hf_hub_download
+from torchaudio.models.decoder import ctc_decoder
+# https://github.com/facebookresearch/fairseq/tree/main/examples/mms/zero_shot
+
+ASR_SAMPLING_RATE = 16_000
+
+WORD_SCORE_DEFAULT_IF_LM = -0.18
+WORD_SCORE_DEFAULT_IF_NOLM = -3.5
+LM_SCORE_DEFAULT = 1.48
+
+MODEL_ID = "mms-meta/mms-zeroshot-300m"
+
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = Wav2Vec2ForCTC.from_pretrained(MODEL_ID)
+
+token_file = hf_hub_download(
+    repo_id=MODEL_ID,
+    filename="tokens.txt",
+)
+
+lm5gram =  hf_hub_download(
+    repo_id="alakxender/w2v-bert-2.0-dhivehi-syn",
+    filename="language_model/5gram.bin",
+)
+
+lexicon_file =  hf_hub_download(
+    repo_type="dataset",
+    repo_id="alakxender/dv-domain-lexicons",
+    filename="dv.domain.news.small.v1.lexicon",
+)
+
+@spaces.GPU
+def transcribe(
+    audio_data,
+    wscore=None,
+    lmscore=None,
+    wscore_usedefault=True,
+    lmscore_usedefault=True,
+    uselm=True,
+    reference=None,
+):
+
+    if not audio_data:
+        yield "ERROR: Empty audio data"
+        return
+
+    # audio
+    if isinstance(audio_data, tuple):
+        # microphone
+        sr, audio_samples = audio_data
+        audio_samples = (audio_samples / 32768.0).astype(float)
+
+        if sr != ASR_SAMPLING_RATE:
+            audio_samples = librosa.resample(
+                audio_samples, orig_sr=sr, target_sr=ASR_SAMPLING_RATE
+            )
+    else:
+        # file upload
+        assert isinstance(audio_data, str)
+        audio_samples = librosa.load(audio_data, sr=ASR_SAMPLING_RATE, mono=True)[0]
+
+    inputs = processor(
+        audio_samples, sampling_rate=ASR_SAMPLING_RATE, return_tensors="pt"
+    )
+
+    # set device
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+    else:
+        device = torch.device("cpu")
+
+    model.to(device)
+    inputs = inputs.to(device)
+
+    with torch.no_grad():
+        outputs = model(**inputs).logits
+        
+    # params
+    if uselm == True:
+        lm_path=lm5gram
+    else:
+        lm_path=None
+    
+    if lm_path is not None and not lm_path.strip():
+        lm_path = None
+
+    if wscore_usedefault:
+        wscore = (
+            WORD_SCORE_DEFAULT_IF_LM
+            if lm_path is not None
+            else WORD_SCORE_DEFAULT_IF_NOLM
+        )
+    if lmscore_usedefault:
+        lmscore = LM_SCORE_DEFAULT if lm_path is not None else 0
+
+    beam_search_decoder = ctc_decoder(
+            lexicon=lexicon_file,
+            tokens=token_file,
+            lm=lm_path,
+            nbest=1,
+            beam_size=500,
+            beam_size_token=50,
+            lm_weight=lmscore,
+            word_score=wscore,
+            sil_score=0,
+            blank_token="<s>",
+        )
+
+    beam_search_result = beam_search_decoder(outputs.to("cpu"))
+    transcription = " ".join(beam_search_result[0][0].words).strip()
+
+    yield transcription
+
+styles = """
+.thaana textarea {
+    font-size: 18px !important;
+    font-family: 'MV_Faseyha', 'Faruma', 'A_Faruma' !important;
+    line-height: 1.8 !important;
+}
+.textbox2 textarea {
+    display: none;
+}
+"""
+
+with gr.Blocks(css=styles) as demo:
+    gr.Markdown("# <center> Transcribe Dhivehi Audio with MMS-ZEROSHOT</center>")
+    with gr.Row():
+        with gr.Column():
+            audio = gr.Audio(label="Audio Input\n(use microphone or upload a file)",min_length=1,max_length=60)
+
+            with gr.Accordion("Advanced Settings", open=False):
+                gr.Markdown(
+                    "The following parameters are used for beam-search decoding. Use the default values if you are not sure."
+                )
+                with gr.Row(): 
+                    with gr.Column():
+                        wscore_usedefault = gr.Checkbox(
+                            label="Use Default Word Insertion Score", value=True
+                        )
+                        wscore = gr.Slider(
+                            minimum=-10.0,
+                            maximum=10.0,
+                            value=WORD_SCORE_DEFAULT_IF_LM,
+                            step=0.1,
+                            interactive=False,
+                            label="Word Insertion Score",
+                        )
+
+                    with gr.Column():
+                        lmscore_usedefault = gr.Checkbox(
+                            label="Use Default Language Model Score", value=True
+                        )
+                        lmscore = gr.Slider(
+                            minimum=-10.0,
+                            maximum=10.0,
+                            value=LM_SCORE_DEFAULT,
+                            step=0.1,
+                            interactive=False,
+                            label="Language Model Score",
+                        )
+                    with gr.Column():
+                        uselm = gr.Checkbox(
+                            label="Use LM",
+                            value=True,
+                        )
+            btn = gr.Button("Submit", elem_id="submit")
+
+            @gr.on(
+                inputs=[wscore_usedefault, lmscore_usedefault, uselm],
+                outputs=[wscore, lmscore],
+            )
+            def update_slider(ws, ls, lm, alm):
+
+                ws_slider = gr.Slider(
+                    minimum=-10.0,
+                    maximum=10.0,
+                    value=LM_SCORE_DEFAULT if (lm is not None or alm) else 0,
+                    step=0.1,
+                    interactive=not ws,
+                    label="Word Insertion Score",
+                )
+                ls_slider = gr.Slider(
+                    minimum=-10.0,
+                    maximum=10.0,
+                    value=WORD_SCORE_DEFAULT_IF_NOLM
+                    if (lm is None and not alm)
+                    else WORD_SCORE_DEFAULT_IF_LM,
+                    step=0.1,
+                    interactive=not ls,
+                    label="Language Model Score",
+                )
+                return ws_slider, ls_slider
+
+        with gr.Column():
+            text = gr.Textbox(label="Transcript",rtl=True,elem_classes="thaana")
+
+    reference = gr.Textbox(label="Reference Transcript", visible=False)
+
+    btn.click(
+        transcribe,
+        inputs=[
+            audio,
+            wscore,
+            lmscore,
+            wscore_usedefault,
+            lmscore_usedefault,
+            uselm,
+            reference,
+        ],
+        outputs=[text],
+    )
+
+    # Examples
+    gr.Examples(
+        examples=[
+            [
+                "samples/audio1.mp3",
+                "އަޅުގަނޑުވެސް ދާކަށް ބޭނުމެއްނުވި"
+            ],
+            [
+                "samples/audio2.wav",
+                "ރަނގަޅަށްވިއްޔާ އެވާނީ މުސްކުޅި ކުރެހުމަކަށް"
+            ],
+
+            [
+                "samples/audio3.wav",
+                "އެއީ ޞަހްޔޫނީންގެ ޒަމާންވީ ރޭވުމެއްގެ ދަށުން މެދުނުކެނޑި ކުރިއަށްވާ ޕްރޮގްރާމެއް"
+            ],
+        ],
+        inputs=[audio, reference],
+        label="Dhivehi Audio Samples",
+    )
+  
+demo.launch(show_api=False)

requirements.txt

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+transformers
+flashlight-text
+librosa
+torchaudio